Device for assessing and managing a health impact of an indoor environment at a site location

A system and a method include receiving, by a processor, from environmental sensors, environmental output data measurements. The environmental sensors are located at a site location. An health impact scoring algorithm computes a plurality of health impact scores from the environmental output data measurements. An overall health impact score at the site location is computed from the any of the health impact scores having a lowest value. A machine learning model generates at least one recommendation for remediating at least one verified environmental hazard type. At least one of the overall health impact score, the at least one verified environmental hazard type, or the at least one recommendation are displayed on a computing device. An instruction is sent to environment-controlling equipment located at the site location to change an operational parameter of the environment-controlling equipment to mitigate the at least one verified environmental hazard type.

FIELD OF TECHNOLOGY

The present disclosure generally relates to a device for assessing and managing the health impact of an indoor environment at a site location.

BACKGROUND OF TECHNOLOGY

A computer network platform/system may include a group of computers (e.g., clients, servers) and other computing hardware devices that are linked together through one or more communication channels to facilitate communication and/or resource-sharing, via one or more specifically programmed graphical user interfaces (GUIs) of the present disclosure, among a wide range of users.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides an exemplary technically improved computer-based method that includes at least the following steps of receiving, by a processor, over a communication network, from a plurality of environmental sensors, a plurality of environmental output data measurements of a plurality of environmental parameters. The plurality of environmental sensors is located inside of at least one site location, outside of the at least one site location, or both. The plurality of environmental sensors comprises:(i) an ozone sensor, outputting an ozone output data measurement of the plurality of environmental output data measurements,(ii) a humidity sensor, outputting a humidity output data measurement of the plurality of environmental output data measurements,(iii) a temperature sensor, outputting a temperature output data measurement of the plurality of environmental output data measurements,(iv) a carbon dioxide sensor, outputting a carbon dioxide output data measurement of the plurality of environmental output data measurements,(v) a carbon monoxide sensor, outputting a carbon monoxide output data measurement of the plurality of environmental output data measurements,(vi) a nitrous dioxide sensor, outputting a nitrous dioxide output data measurement of the plurality of environmental output data measurements,(vii) a sulfur dioxide sensor, outputting a sulfur dioxide output data measurement of the plurality of environmental output data measurements,(viii) a total volatile organic compound (tVOC) sensor, outputting a tVOC output data measurement of the plurality of environmental output data measurements, and(ix) at least one particulate matter sensor, outputting at least one particulate matter output data measurement of the plurality of environmental output data measurements;
A health impact scoring algorithm may be executed, by the processor, to compute a plurality of health impact scores based at least in part on the plurality of environmental output data measurements from the plurality of environmental sensors. A humidity health impact score of the plurality of health impact scores may be based at least in part on the humidity output data measurement. An ozone health impact score of the plurality of health impact scores may be based at least in part on the ozone output data measurement. A temperature health impact score of the plurality of health impact scores may be based at least in part on the temperature output data measurement. A carbon dioxide health impact score of the plurality of health impact scores may be based at least in part on the carbon dioxide output data measurement. A carbon monoxide health impact score of the plurality of health impact scores may be based at least in part on the carbon monoxide output data measurement. A nitrous dioxide health impact score of the plurality of health impact scores may be based at least in part on the nitrous dioxide output data measurement. A sulfur dioxide health impact score of the plurality of health impact scores may be based at least in part on the sulfur dioxide output data measurement. A tVOC health impact score of the plurality of health impact scores may be based at least in part on the tVOC output data measurement. At least one particulate matter health impact score of the plurality of health impact scores may be based at least in part on the at least particulate matter output data measurement. An overall health impact score in the at least one site location may be computed by the processor based at least in part on any of the plurality of health impact scores having a lowest health impact score. At least one verified environmental hazard type in the at least one site location may be determined by the processor when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter. At least one machine learning model, may generate by the processor at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores into the at least one machine recommendation learning model. At least one of may be transmitted by the processor:(A) at least one first instruction to display on a computing device at least one of:(i) the overall health impact score,(ii) the at least one verified environmental hazard type, or(iii) the at least one recommendation, or(B) at least one second instruction to at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.

In some embodiments, the present disclosure provides an exemplary technically improved computer-based system that includes at least the following components a memory and a processor. The processor may be configured to execute computer code stored in the memory that causes the processor to receive over a communication network, from a plurality of environmental sensors, a plurality of environmental output data measurements of a plurality of environmental parameters, where the plurality of environmental sensors may located inside of at least one site location, outside of the at least one site location, or both, wherein the plurality of environmental sensors may include:(i) an ozone sensor, outputting an ozone output data measurement of the plurality of environmental output data measurements,(ii) a humidity sensor, outputting a humidity output data measurement of the plurality of environmental output data measurements,(iii) a temperature sensor, outputting a temperature output data measurement of the plurality of environmental output data measurements,(iv) a carbon dioxide sensor, outputting a carbon dioxide output data measurement of the plurality of environmental output data measurements,(v) a carbon monoxide sensor, outputting a carbon monoxide output data measurement of the plurality of environmental output data measurements,(vi) a nitrous dioxide sensor, outputting a nitrous dioxide output data measurement of the plurality of environmental output data measurements,(vii) a sulfur dioxide sensor, outputting a sulfur dioxide output data measurement of the plurality of environmental output data measurements,(viii) a total volatile organic compound (tVOC) sensor, outputting a tVOC output data measurement of the plurality of environmental output data measurements, and(ix) at least one particulate matter sensor, outputting at least one particulate matter output data measurement of the plurality of environmental output data measurements,
to execute a health impact scoring algorithm to compute a plurality of health impact scores based at least in part on the plurality of environmental output data measurements from the plurality of environmental sensors, where a humidity health impact score of the plurality of health impact scores may be based at least in part on the humidity output data measurement, where an ozone health impact score of the plurality of health impact scores may be based at least in part on the ozone output data measurement, where a temperature health impact score of the plurality of health impact scores may be based at least in part on the temperature output data measurement, where a carbon dioxide health impact score of the plurality of health impact scores may be based at least in part on the carbon dioxide output data measurement, where a carbon monoxide health impact score of the plurality of health impact scores may be based at least in part on the carbon monoxide output data measurement, where a nitrous dioxide health impact score of the plurality of health impact scores may be based at least in part on the nitrous dioxide output data measurement, where a sulfur dioxide health impact score of the plurality of health impact scores may be based at least in part on the sulfur dioxide output data measurement, where a tVOC health impact score of the plurality of health impact scores may be based at least in part on the tVOC output data measurement, where at least one particulate matter health impact score of the plurality of health impact scores may be based at least in part on the at least one particulate matter output data measurement, to compute an overall health impact score in the at least one site location based at least in part on any of the plurality of health impact scores having a lowest health impact score, to determine at least one verified environmental hazard type in the at least one site location when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter, to generate by at least one machine learning model, at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores into the at least one machine learning model, and to transmit over the communication network, at least one of:(A) at least one first instruction to display on a computing device at least one of:(i) the overall health impact score,(ii) the at least one verified environmental hazard type, or(iii) the at least one recommendation, or(B) at least one second instruction to at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.

DETAILED DESCRIPTION

Embodiments of the present disclosure describe system using a device for assessing and managing a health impact of an indoor environment at a site location. The indoor environment at the site location in which at least one person occupies may have an impact on the health of the at least one person. The computer-based systems and methods disclosed herein may optimize a plurality of environmental factors at the site location so as to ensure safe and healthy indoor environments for its occupants. For example, the system and methods described herein may be used to optimize indoor environments. The optimized indoor environments may not only reduce the probability of infectious aerosol transmission, but may also promote a robust immune system and mitigate health problems, such as for example, fatigue, decreased cognitive functioning, stress, anxiety and depression, allergies and autoimmune disorders, dermatitis, bronchospasm and reactive airway disease (asthma), respiratory inflammation, eye irritation and excessive tearing.

The term site location may refer to at least one room or indoor environment in a house, building, and/or buildings at one or more locations that may be monitored by a central monitoring facility, as well as the outdoor area proximal to the at least one room or indoor environment at the site location.

FIG. 1is a diagram of a system10for assessing and managing a health impact of an indoor environment at a site location in accordance with one or more embodiments of the present disclosure. The system10may include a server15, a plurality of N sensor-based (SB) computing devices70A and70B denoted UNIT1and UNIT N, where N is an integer at a respective plurality of N site locations20A and20B, and a mobile computing device95with a graphic user interface (GUI)96on a display associated with a user90that all communicate35over a communication network30. The mobile computing device associated with the user may also be a computer, tablet, laptop, for example with a GUI96. Note thatFIG. 1illustrates, merely for conceptual clarity and not by way of limitation, one sensor-based computing device at one site location, but there may be any suitable number of sensor-based computing devices at a single site location.

In some embodiments, the server15may include a processor20, a memory55, input and output (I/O) devices45and a communication circuitry50for enabling the server15to communicate35over the communication network30. The processor20may execute software modules to enable the functions of the system10as described herein. The software modules may include a sensor data collection22module for collecting sensor data from each of the N sensor-based computing devices over the communication network30, a health impact score algorithm24for computing health impact (HI) scores26for the collected sensor data, a machine learning model28trained to output remediation recommendations30, an environmental controlling equipment (ECE) manager35for sending instructions over the communication network for controlling environment controlling equipment at the site location, and a GUI controller40for sending instructions to display, for example, health impact scores, remediation recommendations, and/or any other suitable metrics on any of the graphic user interfaces (GUIs) used in the system10. The memory55may store a breakpoint value60database, where the breakpoint values may be used to compute the health impact scores and/or a historical data65database may be used to store any suitable parameters needed to display to a user, the historical health impact scores over any desired time interval. The memory55may store any suitable database for storing data for performing the functions described herein.

In some embodiments, at the site location120A, a sensor-based (SB) computing device170A may be located. The SB computing device170A may include a plurality of sensors72A denoted SENSORS1, conditioning and sensor data driver circuitry174A for conditioning the sensor data by converting the analog output data from the sensors to digital sensor data via analog-to-digital converters, input and output (I/O) devices177A, such as a display178A, a microphone and/or speaker (not shown), and/or control buttons179A, for example, a processor175A that may be configured to display a GUI180A on the display178A, a communication circuitry182A for enabling the SB computing device to communicate35over the communication network30, and/or an environmental controlling equipment controller185A circuitry for controlling equipment at the site location for optimizing the environment in which the SB computing device182A is located. The circuitry of the SB computing device170A may be powered by a battery, a solar cell, and/or via a connection a mains electricity such as via a wall outlet, for example. The control buttons179A may be used for powering UNIT1on and off, changing any readouts on the display178A, or used for any suitable purpose.

In some embodiments, similarly at the Nth site location20B, a sensor-based (SB) computing deviceN70B may be located. The SB computing deviceN70B may include a plurality of sensors72B denoted SENSORS N, conditioning and sensor data driver circuitry N74B for conditioning the sensor data by converting the analog output data from the sensors to digital sensor data via analog-to-digital converters, input and output (I/O) devicesN77B, such as a displayN78B, a microphone and/or speaker (not shown), and/or control buttons N79B, for example, a processorN75B that may be configured to display a GUI N80B on the display N78B, a communication circuitry N82B for enabling the SB computing device to communicate35over the communication network30, and/or an environmental controlling equipment controller N85B circuitry for controlling equipment at the site location for optimizing the environment in which the SB computing device N70B is located. The circuitry of the SB computing deviceN70B may be powered by a battery, a solar cell, and/or via a connection a mains electricity such as via a wall outlet, for example. The control buttons N79B may be used for powering UNITN on and off, changing any readouts on the displayN78B, or used for any suitable purpose.

It should be noted that the Nth SB computing deviceN70B and its elements may be referred to hereinafter as SB computing device70at the site location20, the processor75controlling a GUI80, etc.

FIG. 2schematically illustrates the sensors-based computing device70at an exemplary site location100in accordance with one or more embodiments of the present disclosure. The exemplary site location100may be a room in a building with computer-controlled environment control equipment. The sensor-based computing device70may communicate35over the communication network30with the server30. The SB computing device70may relay sensor data (e.g., typically digitized sensor data) received from the plurality of sensors over the communication network to the sensor data collection22module in the server15. Subsequently, the SB computing device70may receive a first instruction from the GUI controller40over the communication network30to display recommendations (e.g., “INCREASE THE ROOM TEMPERATURE”) on the GUI80and/or to activate an audible alert such as, for example, when an environmental hazard type in the room is detected and verified. Similarly, the SB computing device70may receive a second instruction from the ECE manager35over the communication network30to change an operational parameter environment control equipment located at the site location so as to mitigate the environmental hazard type in the room.

In some embodiments, the site location100may include the computer-controlled environment control equipment such as, for example, a computer-controlled thermostat110, a computer-controlled vent120to vent air between the interior to the room to the outside, a computer-controlled humidifier/dehumidifier unit130, a computer-controlled ceiling fan for dispersing indoor air, a window150that may be opened or closed by a computer-controlled motorized window actuator155, and/or a computer-controlled air filter160in the room. Each of the computer-controlled environment control equipment may have circuitry to allow each of the computer-controlled environment control equipment to communicate105with the sensor-based computing device70. In other embodiments, any or all of the computer-controlled environment control equipment may be operated manually, for example, by the user90receiving a remediation recommendation to “open the window”, for example, either on GUI80and/or GUI96.

The embodiments shown inFIGS. 1 and 2are merely for visual and conceptual clarity and not by way of the embodiments disclosed herein. The SB computing device70may include only sensors, processing and communication circuitry without a display such that the SB computing device70may merely sense the environment at the site location using the plurality of sensors and may relay the sensor data to the server15as shown in the example ofFIG. 3below. Subsequently, the server15may issue alerts to a user90via the GUI96on the mobile computing device associated with a user90, for example. The user90may be an occupant of the room. The alert may be an audible alarm, a visual alert, an electronic alert message, or any combination thereof.

FIG. 3is a diagram of an exemplary SB computing device170in accordance with one or more embodiments of the present disclosure. The SB computing device170may include circuitry deployed within a case175formed from any suitable material. The circuitry may include the plurality of sensors72, the conditioning and driver circuitry74, the processor75and the communication circuitry82for communicating35over the communication network30. A power cord180may be connected to wall output for example, to power the circuitry. The case175may include a plurality of holes185formed in the case175for allowing each of the plurality of sensors72to contact and/or sample the environment proximal to the SB computing device170.

In some embodiments, the SB computing device170and/or the SB computing device70may communicate35over a Wireless Fidelity (Wi-Fi) network, or any suitable communication protocol. The sensor data from each SB computing device may be captured and then transmitted to a cloud environment such as Amazon Web Services (AWS), for example.

In some embodiments, the server15(e.g., the sensor data collection22module) may use an application programming interface (API) and data fetcher that reaches into the TSI cloud (e.g., cloud computing services from TSI healthcare) and pulls the data need by the health impact score algorithm24, which is subsequently loaded to the cloud environment (e.g., AWS). The TSI cloud may continuously receive the data from the sensors, but the sensor data collection22module may pull the needed data to the AWS cloud at predefined time intervals such as every 15 minutes.

In some embodiments, the SB computing device170as shown inFIG. 3may have dimensions of 6-7 inches long and 5 inches wide with a thickness of 1 inch. The plurality of sensors72in the SB computing device170may sense environment hazards at a site location (e.g., a room) covering an area ranging from 1000-5000 square feet.

In some embodiments, once a particular SB computing device170may detect at least one verified environment health hazard type when the processor20analyzes the sensor data from the plurality of sensors in the particular SB computing device170located in a particular room, the server15may issue alerts to the GUI96of the computing devices associated with the users that are the occupants of the particular room.

In some embodiments, the server15may send batch alerts to the GUI96on a plurality of computing devices95associated a plurality of users where each of the plurality of users may be at least one site location with at least one verified environmental hazard type. For example, the server15may track the details of each user that have entered the building and their location in the building using location tracking devices, for example, in the user's computing device95.

In some embodiments, a user computing device95may be a central facility computing device of a business or company. A plurality of SB computing devices70may be deployed in a plurality of site locations20(e.g., rooms) in a plurality of buildings at different geographical locations associated with the business. The plurality of SB computing devices70may relay the sensor data in each room in each building to the central facility computing device associated with the business or company. The GUI96may display the status of the environment and may alert a maintenance officer of the business or company about the presence of any verified health hazard types in any of the rooms.

In some embodiments, the company may be, for example, a very large nursing home company that has hundreds of nursing homes, and/or extended care facilities. The user such as a maintenance officer may see a home screen on GUI96that may show a map of the United States with click buttons in each location around the country where a particular nursing home and/or extended care facility may be located. The maintenance officer in a central facility in New York, for example, may be able to access a page for a nursing home in Orlando, Fla., for example, and further drill down to a particular room in the Orlando nursing home to view the health statistics that are sensed by a particular SB computing device from the plurality of SB computing devices70in the particular room.

FIG. 4is an exemplary snapshot200of the graphic user interface (GUI)96displayed on the display95in accordance with one or more embodiments of the present disclosure. The exemplary snapshot200in this example may refer to a building with a plurality of rooms. The SB computing device170such as shown inFIG. 3may be deployed in each room. The computing device95associated with the user90, such as the building manager or handyman maintaining the building, may receive environment health sensor updates on the overall building205and in a particular room207. The GUI96may include an overall health impact rating (HIR)230in the building (e.g., NSIC Business Park in this exemplary case), a thermal HIR210based on the temperature detected in the rooms of building environment, a gases HIR220based on gases detected in the rooms of the building environment, and a particle HIR215based on particulate matter or particles detected in the air in the rooms of the building environment.

In some embodiments, the health impact ratings detected from the SB computing device170for a particular room207(e,g, Location—Office Room 2 in the NSIC Business Park Building) may be identified by a device identification number208(e.g., 81442116019 in this exemplary case). The GUI96on unit in the particular room207shows that the overall HIR240for this room is 30, the thermal HIR250for this room is 30, the gases HIR255for this room is 65 and the particle HIR60for this room is 76.

In some embodiments, the HIR may indicate a level of environmental hazard of a particular type such as thermal, gases, and/or particles (e.g., particulate matter in the air). The HIR rating may include four health impact quartiles, for example, in a particular environment where an HIR of 75-100 is Excellent, an HIR of 50-75 is Good to Acceptable, an HIR of 25-50 Needs Remediation, and an HIR of 0-25 is Hazardous. In other embodiments, the HIR ranges may be defined as 0-24 as being unhealthy, 25-49 as being moderate, and 50-100 as healthy. Any suitable range definition may be used.

In some embodiments, predefined thresholds for the overall, particle, gases and particle HIR may be defined for example at a value of 49, for example. Thus the SB computing device170may issue an alert270due to detecting an unhealthy temperature HIR rating of 30 in the particular room207(e,g, Location—Office Room 2 in the NSIC Business Park Building).

In some embodiments, the GUI96may display an overall health impact rating history275of the entire building or a particular room over a time period of a week, a month, and/or a year that the user may choose on the GUI96. Similarly, the GUI96may display a historical thermal HIR295, a historical gases HIR285, and a historical particle HIR290.

In some embodiment, the GUI96may activate an icon207indicating that the outdoor air quality outside of the building is good based on measurements from the external sensors. In other embodiments, the SB computing device170unit may be placed outside of the building to compare the air quality inside of site location (e.g., inside the building) to the air quality outside of the site location (e.g., outside of the building).

FIG. 5is a flow diagram300using a plurality of health sensors in an health impact score algorithm320in accordance with one or more embodiments of the present disclosure. The plurality of health sensors72may include an ozone sensor, outputting an ozone output data measurement314, a humidity sensor, outputting a humidity or a relative humidity RH output data measurement318, a temperature sensor, outputting a temperature output data measurement (TEMP)316, a carbon dioxide sensor, outputting a carbon dioxide (CO2) output data measurement306, a carbon monoxide sensor, outputting a carbon monoxide (CO) output data measurement304, a nitrous dioxide sensor, outputting a nitrous dioxide (NO2) output data measurement308, a sulfur dioxide sensor, outputting a sulfur dioxide (SO2) output data measurement310, a total volatile organic compound (tVOC) sensor, outputting a VOCs output data measurement312, and at least one particulate matter sensor, outputting at least one particulate matter output data measurement (PARTICLES)302.

Note that the ten unique environmental sensor types for collecting the ten sensor data values have been chosen based on the individual and interacting impact of measured components on human physiology and microbial viability. The sensor data from each sensor type may be weighted according to its influence on other indoor pollutants. If sensor data from a particular sensor type is not collected due to sensor malfunction, for example, the algorithms may become less comprehensive. Conversely, if a sensor detects a low insignificant level of a given pollutant, the integrity of the algorithm may still be maintained. Note that the use often unique environmental sensor types is not by way of limitation of the embodiments disclosed herein, any suitable number of unique environmental sensor types may be used.

In some embodiments, the total volatile organic compound (tVOC) sensor may be configured to detect specific volatile organic compounds in the air such as phenol, toluene, trichlorobenzenes, trichloroethylene, vinyl chloride, and/or formaldehyde, for example.

In some embodiments. the at least one particulate matter sensor may include a 10 μm PM10particulate matter sensor, a 2.5 μm PM2.5particulate matter sensor, and a 1 μm PM1particulate matter sensor. Thus the at least one particulate matter output data measurement (PARTICLES)302may be based on a PM2.5particulate matter output data measurement, a PM2.5particulate matter output data measurement, and a PM1particulate matter output data measurement.

Each of the plurality of output data measurements from each of the respective sensors may be inputted into an health impact score algorithm320(or the health impact score algorithm24ofFIG. 1). Each of the health impact scores for each pollutant may be outputted from the health impact score algorithm320. In some embodiments, the HIR values may be calculated when normalizing the HI(f) values to an ozone and relative humidity factor based on the ozone output data measurement314, and the relative humidity RH output data measurement318.

In some embodiments, the health impact score HI(RH) related to the relative humidity sensor output data measurement318may be computed from equation (1) where CRHis the relative humidity concentration obtained from the relative humidity sensor output data measurement318, breakpoint values BPHiand BPLoof CRHand index breakpoint values IHiof BPHiand ILoof BPLo, constants that may be obtained from medical studies and/or exposure studies. These breakpoint values may be stored in the breakpoint value database60.

Similarly, the health impact score HI(O3) related to the ozone sensor output data measurement318may be computed from equation (2) where CO3is the ozone concentration obtained from the ozone sensor output data measurement314, RHfis the relative humidity factor based on Equation (1), breakpoint values BPHiand BPLoof CO3and index breakpoint values of BPHiand ILoof BPLo, constants that may be obtained from medical studies and/or exposure studies. These breakpoint values may be stored in the breakpoint value database60.

Finally, the health impact ratings HI(f) may be computed using Equation (3) for each of the other f pollutants may be based on the pollutant concentration Cf, the relative humidity factor RHf, and an ozone factor O3fThe other f pollutants may include particulate matter PARTICLES, carbon monoxide CO, carbon dioxide CO2, nitrous dioxide NO2, sulfur dioxide SO2, total volatile organic compound tVOCs, and temperature TEMP each having respective concentrations denoted by Cf={CPM10, CPM2.5, CPM1, CtVOC, CNO2, CSO2, CCO, CCO2, CT}

Thus, the health impact score algorithm may compute using Equations (1)-(3), the set of health index score (HI) values including {a 10 μm particulate matter health index score HI(PM10), a 2.5 μm particulate matter health index score HI(PM2.5), a 1 μm particulate matter health index score HI(PM1), a total volatile organic compound health index score HI(tVOC), a nitrous dioxide health index score HI(NO2), a sulfur dioxide health index score HI(SO2), a carbon monoxide health index score HI(CO), an ozone health index score HI(O3), a carbon dioxide health index score HI(CO2), a relative humidity health index score HI(RH), a temperature health index score HI(Temp)}. Each of the health index scores is based is least in part on the environmental output data measurements from the plurality of environmental sensors.

As shown inFIG. 4, the HIR for gases220may be the lowest HI value related to gases in the set of HI values (e.g., HI(tVOC), HI(NO2), HI(SO2), HI(CO), HI(O3), HI(CO2)), the HIR for particles215may be the lowest HI value related to particles (e.g., HI(PM10), HI(PM2.5), HI(PM1)) in the set of HI values, the HIR for thermal conditions210is HI(Temp). The overall HIR330may be derived from the lowest value in the set of HI values, e.g., the lowest of any one of {HI(PM10), HI(PM2.5), HI(PM1), HI(tVOC), HI(NO2), HI(SO2), HI(CO), HI(O3), HI(CO2), HI(RH), HI(Temp)}.

In some embodiments, the health impact score algorithm320may use short term acceptable exposure levels making the limits conservative and safest for each health impact zone. Continuous monitoring may be used to capture all conditions where each of the HI values in the set above is weighted according to interactions with concurrent relative humidity and ozone to establish a single, dynamic, multipollutant aggregated health impact rating. As a result, pollutant concentrations may be related to the safest international medical thresholds.

FIG. 6is a table400showing the breakpoint values for different pollutants in accordance with one or more embodiments of the present disclosure. The aggregate HI values may be determined by selecting the pollutant with the highest pollutant index so as to avoid exaggerated measurements (ambiguity) found with adding HI values or lack of action (eclipsing) found with averaging HI values. Thus, the overall HIR is computed using the lowest HI value at a particular site location and not by averaging, adding, or multiplying individual pollutant indices. The HI values may be used to create a continuous scale which can be categorized as good460, normal465, unhealthy for sensitive groups470, unhealthy475, very unhealthy480, and hazardous485) which are used in the computation of the HI(f) values. The health impact score (HI) values may be normalized to create a health impact rating (HIR).

In the table400, the breakpoint values BPLOand BPHIare for ozone410, for PM1.0415, for PM2.5420, for PM10425, for CO430, for CO2435, for total VOCs440, for SO2445, and for NO2450. Each shows breakpoint values in each of the six zones.

FIG. 7is a table500showing a second embodiment for computing health impact scores in accordance with one or more embodiments of the present disclosure. The equations shown for computing the HI(f) scores are a different, second embodiment for computing HI(f) in contrast to Equations (1)-(3). In this exemplary second embodiment, the HI scores may be measured having maximum exposure levels taken over a predefined time interval such as eight hours, for example. There is no need to consider the six threshold regions as shown inFIG. 6for use in the first HI(f) model. In this case, the HI(f) model may use a continuous slope model that may be fit to the HI score data using two fitting polynomials. Thus, each pollutant, or measured compound (f)505in table500for each HI(f)510may have a unique equation 520 as shown inFIG. 7in contrast to Equation (3) in the first HI(f) model. Each equation may be a function of the pollutant concentration value and the square of the pollutant concentration value. Multi-aggregate weighting530may be applied to the unweighted HI(f) parameter. The weights may be taken from the peer reviewed medical and epidemiological literature including the Cochran database, a public database for healthcare decision-making, to adjust the HI(f) values. The last column shows the total amount540that the HI may be changed by relative humidity.

In some embodiments, each of the health index scores HI described in Equations (1)-(3) and/or inFIG. 7is based is least in part on the environmental output data measurements from the plurality of environmental sensors. In other embodiments, environmental output data measurements may be the measured pollutant concentrations (e.g., CRH, CO3, CPM10, CPM2.5, CPM1, CtVOC, CNO2, CSO2, CCO, CCO2, CT).

It should be noted that the two exemplary health impact algorithms for computing the health impact scores as shown inFIGS. 5-7and defined in equations (1)-(3) are not by way of limitation of the embodiments disclosed herein. Any suitable health impact algorithm may be used by system10and/or the SB computing device N70B to compute the health impact scores as used herein to assess environmental hazards and for automatically managing the indoor environment for optimum occupant health.

FIG. 8is a table600showing human health impact of measured indoor factors in accordance with one or more embodiments of the present disclosure. The table600shows different indoor metrics605such as carbon dioxide, ozone, total VOCs, PM10, PM2.5, PM1, carbon monoxide, nitrogen dioxide, sulfur dioxide, relative humidity, and temperature. A health impact610on the immune system, respiratory disease, cell/tissue damage, organ dysfunction, and microbiome disruption may be shown as a function of each indoor metric and classified (by hashing) as low health hazard and impact620, moderate health hazard and impact630, and high health hazard and impact640.

In some embodiments, once different pollutant concentrations are determined from the plurality of sensors and the health impact scores may be computed, at least one verified environmental hazard type related to any of the pollutants inFIG. 8, for example, may be determined at the site location when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter (e.g., concentrations and/or levels of particulate matter PARTICLES, carbon monoxide CO, carbon dioxide CO2, nitrous dioxide NO2, sulfur dioxide SO2, total volatile organic compound tVOCs, temperature TEMP, ozone, humidity).

In some embodiments, the processor20and/or the sensor based computing device processor75may use the computed set of health impact scores {HI(PM10), HI(PM2.5), HI(PM1), HI(tVOC), HI(NO2), HI(SO2), HI(CO), HI(O3), HI(CO2), HI(RH), HI(Temp)} for each of the different pollutant types to determine if at least one environmental hazard type is present in the site location. The processor20and/or the SB computing device processor75may determine if there is at least one verified environmental hazard type when at least one particular health impact score in the set of HI scores is less than a predefined threshold score that is unique to the at least one particular environmental parameter or pollutant. For example, the predefined threshold score may be different or unique for each pollutant in the set of HI scores. In other embodiments, the predefined threshold score may be the same threshold score for all of the different pollutants.

For example, a health hazard may exist for carbon dioxide when the HI score for carbon dioxide is below a predefined score of 50, whereas the health hazard may exist for ozone when the HI score for ozone is below a predefined score of 70. When HI for a particular pollutant is below the predefined threshold score, then that pollutant become a verified environmental hazard type, where there are carbon dioxide concentrations or sulfur dioxide concentrations, for example, that are too high in the site location and more specifically in the indoor environment at the site location.

In some embodiments, the machine learning model28may generate at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores {HI(PM10), HI(PM2.5), HI(PM1), HI(tVOC), HI(NO2), HI(SO2), HI(CO), HI(O3), HI(CO2), HI(RH), HI(Temp)}into the at least one machine learning model. The at least one recommendation may be executed manually by the user90, for example, or as shown in the embodiments ofFIG. 2, the at least one remediation recommendation may be used to generate instructions which may be transmitted by the ECE manager35over the communication network30to automatically control at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.

In some embodiments, the machine learning model28may be, for example, a classification neural network model trained to map the set of health impact score to remediation recommendations and/or remediation actions for mitigating the at least one verified environmental hazard type as described below with reference to Table I.

In some embodiments, a computer-controlled window actuator (e.g., an environment-controlling equipment) may have the window set to be closed, where the closed window is an operational parameter of the environment-controlling equipment. The SB computing device may detect, for example, that the concentration of tVOCs are too high, the processor20and/or the sensor based computing device processor75may send instructions via the ECE manager35and/or ECE controller85to change the operational parameter of the environment-controlling equipment (e.g., initially closed window and/or closed vent120) by opening the window150and/or opening the ceiling vent120and/or activating the ceiling fan135, for example.

In some embodiments, the following Table I is an exemplary output from the machine learning model28that may be used to generate instructions to transmit to the at least one environment-controlling equipment located at the site location. The computer-controlled equipment may include actuated environmental control devices, for example, with reference toFIG. 2. The instructions may trigger the computer-controlled actuators in the equipment to automatically increase or decrease outdoor air fraction in the room by controlling the vent120, disperse indoor air in the room by controlling the fan135, raise or lower a temperature of the room by controlling the thermostat110, raise or lower humidity in the room by controlling the humidifier/dehumidifier130, activate or deactivate air cleaners in the room by controlled the air filter160, or any combination thereof.

In some embodiments, the machine learning model28may be used to determine which action recommendations are most effective in raising the health impact scores, to predict the times of day that certain pollutant variables are likely to have certain concentrations so that the users may take preventive actions, and/or to fine tune the sensor data according to evolving research.

The remediation recommendation actions generated by the machine learning model28for the different pollutants are shown below in Table I:

TABLE IRemediation recommendation actions (in bold)•Particles○DiluteIf (indoor RH < 40% && outdoor RH > indoor RH && outdoor HIR >=indoor HIR):“Increase outside air fraction to dilute indoor particles”If (indoor RH < 40% && outdoor RH < indoor RH) :“Decrease outdoor air fraction and turn on humidifier (ifavailable) to decrease indoor particles”If (indoor RH > 60% && outdoor RH < indoor RH && outdoor HIR >=indoor HIR):“Increase outdoor air fraction to dilute indoor particles”If (outdoor HIR > indoor HIR) :“Increase outdoor air fraction to dilute indoor particles”○DisperseIf (outdoor HIR < indoor HIR) :“Decrease outdoor air fraction and increase exhaust rate (ifavailable) to disperse indoor particles”○RemoveIf (other remediations don't work):“Replace/Upgrade filters on existing air handling units (ifavailable) to decrease indoor particles”•tVOCs○DiluteIf (indoor RH < 40% && outdoor RH > indoor RH && outdoor HIR >=indoor HIR):“Increase outdoor air fraction to dilute total Volatile OrganicCompounds”If (indoor RH > 60% && outdoor RH < indoor RH && outdoor HIR >=indoor HIR):“Increase outdoor air fraction to dilute total Volatile OrganicCompounds”If (indoor RH > 60% && outdoor RH > indoor RH):“Decrease outdoor air fraction and dehumidify to returnRelative Humidity to between 40-60%”If (indoor temp < 72 degrees F.) :“Increase indoor temperature up to 72° F. to return RelativeHumidity to between 40-60%”If (outdoor HIR > indoor HIR) :“Increase outdoor air fraction to dilute total Volatile OrganicCompounds”○DisperseIf (outdoor HIR < indoor HIR):“Decrease outdoor air fraction and increase exhaust (ifavailable) to disperse total Volatile Organic Compounds”•RH○If (indoor RH > 60% && outdoor RH < 40% && outdoor HIR >= indoor HIR):“Increase outdoor air fraction to increase ventilation to return RelativeHumidity to between 40-60%”○If (indoor RH > 60% && outdoor RH > 60%) :“Turn on dehumidifier (if available) or increase the temperature toreturn Relative Humidity to between 40-60%”○If (indoor RH < 40% && outdoor RH > 40%) :“Increase outdoor air fraction to return Relative Humidity to between40-60%”○If (indoor RH < 40%)“Turn on humidifier (if available) or lower the temperature to returnRelative Humidity to between 40-60%”•Temperature○Too highIf (outdoor temperature is between 68-73° F. && outdoor HIR >= indoorHIR):“Increase outdoor air fraction to decrease temperature.Maintain a temperature between 68-73° F.”Or else:“Decrease the temperature. Maintain a temperature between 68-73° F.”○Too lowIf (outdoor temperature is between 68-73° F. && outdoor HIR >= indoorHIR):“Increase outdoor air fraction to increase temperature.Maintain a temperature between 68-73° F.”Or else:“Increase the temperature. Maintain a temperature between 68-73° F.”•CO2○DiluteIf (outdoor HIR > indoor HIR) :“Increase outdoor air fraction to dilute Carbon Dioxide”○DisperseIf (outdoor HIR < indoor HIR) :“Decrease outdoor air fraction and increase exhaust (ifavailable) to disperse Carbon Dioxide”•O3○DiluteIf (outdoor HIR > indoor HIR) :“Increase outdoor air fraction to dilute Ozone”○DisperseIf (outdoor HIR < indoor HIR) :“Decrease outdoor air fraction and increase exhaust (ifavailable) to disperse Ozone”○Control SourceIf (Dilute and Disperse don't work) :“Turn off electronic devices that may be producing Ozone”•CO○DiluteIf (outdoor HIR > indoor HIR) :“Increase outdoor air fraction to dilute Carbon Monoxide”○DisperseIf (outdoor HIR < indoor HIR) :“Decrease outdoor air fraction and increase exhaust (ifavailable) to limit Carbon Monoxide infiltration”○Control SourceIf (Dilute and Disperse don't work) :“Find and eliminate any source of indoor gas production (forexample cooking fumes or fire in fireplace)”•NO2○“Decrease outdoor air fraction and increase exhaust (if available) to limitNitrogen Dioxide infiltration”•SO2○“Decrease outdoor air fraction and increase exhaust (if available) to limit SulfurDioxide infiltration”

In some embodiments, the remediation recommendation actions shown in bold in Table I may cause the processor20to transmit first instructions to the SB computing device70to display on the display80and/or on the GUI96, a remediation action message and/or to sound an alarm, for example. In other embodiments, the remediation recommendation actions shown in bold in Table I may cause the processor20using the ECE manager35to transmit second instructions to the ECE controller85to automatically change the operational parameter of the environment-controlling equipment as previously described.

FIG. 9illustrates a flowchart of an exemplary method700for assessing and managing a health impact of an indoor environment at a site location in accordance with one or more embodiments of the present disclosure. The method may be performed, for example, by the processor20.

The method700may include receiving710over a communication network, from a plurality of environmental sensors, a plurality of environmental output data measurements of a plurality of environmental parameters, where the plurality of environmental sensors is located inside of at least one site location, outside of the at least one site location, or both.

The method700may include executing720an health impact scoring algorithm to compute a plurality of health impact scores based at least in part on the plurality of environmental output data measurements from the plurality of environmental sensors. A first exemplary embodiment of the health impact scoring algorithm24may be given by Equations (1)-(3) where each of the health impact scores for each of the different pollutants may be further based on any of an ozone factor, a relative humidity factor, and/or predefined pollutant-specific breakpoint hazard values as shown for example inFIG. 6. A second exemplary embodiment of the health impact scoring algorithm24may be further based on using a value from each of the plurality of environmental output data measurements and a square of the value.

The method700may include computing730an overall health impact score in the at least one site location based at least in part on any of the plurality of health impact scores having a lowest health impact score.

The method700may include determining740at least one verified environmental hazard type in the at least one site location when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter.

The method700may include generating750by at least one machine learning model, at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores into the at least one machine recommendation learning model.

The method700may include transmitting760over the communication network, at least one of: (A) at least one first instruction to display on a computing device at least one of: (i) the overall health impact score, (ii) the at least one verified environmental hazard type, or (iii) the at least one recommendation, or (B) at least one second instruction to at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.

The sensor-based computing device70as disclosed herein provides a technical solution for harmonizing a common sensing process using multiple environmental sensors to automatically assess a health impact of an indoor environment at least one site location by computing a plurality of health impact scores based on the multiple environmental sensor output data. If the system10using the sensor-based computing device70verifies that at least one verified environmental hazard type exists, the system10may automatically implement measures for remediating the at least one verified environmental hazard type by integrating the use of the computed health impact scores to automatically cause changes in operational parameter(s) of environment-controlling equipment to mitigate the at least one verified environmental hazard type at least one site location.

In some embodiments, exemplary inventive, specially programmed computing systems/platforms with associated devices are configured to operate in the distributed network environment, communicating with one another over one or more suitable data communication networks (e.g., the Internet, satellite, etc.) and utilizing one or more suitable data communication protocols/modes such as, without limitation, IPX/SPX, X.25, AX.25, AppleTalk™, TCP/IP (e.g., HTTP), near-field wireless communication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitable communication modes. In some embodiments, the NFC can represent a short-range wireless communications technology in which NFC-enabled devices are “swiped,” “bumped,” “tap” or otherwise moved in close proximity to communicate. In some embodiments, the NFC could include a set of short-range wireless technologies, typically requiring a distance of 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to 424 kbit/s. In some embodiments, the NFC can involve an initiator and a target; the initiator actively generates an RF field that can power a passive target. In some embodiments, this can enable NFC targets to take very simple form factors such as tags, stickers, key fobs, or cards that do not require batteries. In some embodiments, the NFC's peer-to-peer communication can be conducted when a plurality of NFC-enable devices (e.g., smartphones) within close proximity of each other.

In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to utilize hardwired circuitry that may be used in place of or in combination with software instructions to implement features consistent with principles of the disclosure. Thus, implementations consistent with principles of the disclosure are not limited to any specific combination of hardware circuitry and software. For example, various embodiments may be embodied in many different ways as a software component such as, without limitation, a stand-alone software package, a combination of software packages, or it may be a software package incorporated as a “tool” in a larger software product.

In some embodiments, exemplary inventive computer-based systems/platforms, exemplary inventive computer-based devices, and/or exemplary inventive computer-based components of the present disclosure may be configured to handle numerous concurrent sensor-based computing devices that may be, but is not limited to, at least 100 (e.g., but not limited to, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), at least 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000 (e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g., but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to, 1,000,000,000-999,999,999,999), and so on.

As used herein, the terms “proximity detection,” “locating,” “location data,” “location information,” and “location tracking” refer to any form of location tracking technology or locating method that can be used to provide a location of, for example, a particular computing device/system/platform of the present disclosure and/or any associated computing devices, based at least in part on one or more of the following techniques/devices, without limitation: accelerometer(s), gyroscope(s), Global Positioning Systems (GPS); GPS accessed using Bluetooth™; GPS accessed using any reasonable form of wireless and/or non-wireless communication; WiFi™ server location data; Bluetooth™ based location data; triangulation such as, but not limited to, network based triangulation, WiFi™ server information based triangulation, Bluetooth™ server information based triangulation; Cell Identification based triangulation, Enhanced Cell Identification based triangulation, Uplink-Time difference of arrival (U-TDOA) based triangulation, Time of arrival (TOA) based triangulation, Angle of arrival (AOA) based triangulation; techniques and systems using a geographic coordinate system such as, but not limited to, longitudinal and latitudinal based, geodesic height based, Cartesian coordinates based; Radio Frequency Identification such as, but not limited to, Long range RFID, Short range RFID; using any form of RFID tag such as, but not limited to active RFID tags, passive RFID tags, battery assisted passive RFID tags; or any other reasonable way to determine location. For ease, at times the above variations are not listed or are only partially listed; this is in no way meant to be a limitation.

In some embodiments, the server15may be a web server (or a series of servers) running a network operating system, examples of which may include but are not limited to Microsoft Windows Server, Novell NetWare, or Linux. In some embodiments, the server15may be used for and/or provide cloud and/or network computing.

In some embodiments, the server15includes a computer-readable medium, such as a random-access memory (RAM) (e.g., the memory55) coupled to a processor or FLASH memory (e.g., the processor20). In some embodiments, the processor20may execute computer-executable program instructions stored in the memory. In some embodiments, the processor20may include a microprocessor, an ASIC, and/or a state machine. In some embodiments, the processor may include, or may be in communication with, media, for example computer-readable media, which stores instructions that, when executed by the processor20, may cause the processor20to perform one or more steps described herein. In some embodiments, examples of computer-readable media may include, but are not limited to, an electronic, optical, magnetic, or other storage or transmission device capable of providing a processor, such as the processor20, with computer-readable instructions. In some embodiments, other examples of suitable media may include, but are not limited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions. Also, various other forms of computer-readable media may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless. In some embodiments, the instructions may comprise code from any computer-programming language, including, for example, C, C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, the I/O devices45and/or the I/O devices77A and77B may also comprise a number of external or internal devices such as a mouse, a CD-ROM, DVD, a physical or virtual keyboard, a display, a speaker, or other input or output devices.

In some embodiments, the exemplary inventive computer-based systems/platforms, the exemplary inventive computer-based devices, and/or the exemplary inventive computer-based components of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques (e.g., the machine learning model28) chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary neutral network technique may be one of, without limitation, feedforward neural network, radial basis function network, recurrent neural network, convolutional network (e.g., U-net) or other suitable network. In some embodiments and, optionally, in combination of any embodiment described above or below, an exemplary implementation of Neural Network may be executed as follows:

ii) Transfer the input data to the exemplary neural network model,

iii) Train the exemplary model incrementally,

iv) determine the accuracy for a specific number of timesteps,

v) apply the exemplary trained model to process the newly-received input data,

vi) optionally and in parallel, continue to train the exemplary trained model with a predetermined periodicity.

At least some aspects of the present disclosure will now be described with reference to the following numbered clauses.

1. A method may include:

receiving, by a processor, over a communication network, from a plurality of environmental sensors, a plurality of environmental output data measurements of a plurality of environmental parameters;where the plurality of environmental sensors may be located inside of at least one site location, outside of the at least one site location, or both;wherein the plurality of environmental sensors may include:(i) an ozone sensor, outputting an ozone output data measurement of the plurality of environmental output data measurements,(ii) a humidity sensor, outputting a humidity output data measurement of the plurality of environmental output data measurements,(iii) a temperature sensor, outputting a temperature output data measurement of the plurality of environmental output data measurements,(iv) a carbon dioxide sensor, outputting a carbon dioxide output data measurement of the plurality of environmental output data measurements,(v) a carbon monoxide sensor, outputting a carbon monoxide output data measurement of the plurality of environmental output data measurements,(vi) a nitrous dioxide sensor, outputting a nitrous dioxide output data measurement of the plurality of environmental output data measurements,(vii) a sulfur dioxide sensor, outputting a sulfur dioxide output data measurement of the plurality of environmental output data measurements,(viii) a total volatile organic compound (tVOC) sensor, outputting a tVOC output data measurement of the plurality of environmental output data measurements, and(ix) at least one particulate matter sensor, outputting at least one particulate matter output data measurement of the plurality of environmental output data measurements;

executing, by the processor, a health impact scoring algorithm to compute a plurality of health impact scores based at least in part on the plurality of environmental output data measurements from the plurality of environmental sensors;where a humidity health impact score of the plurality of health impact scores may be based at least in part on the humidity output data measurement;where an ozone health impact score of the plurality of health impact scores may be based at least in part on the ozone output data measurement;where a temperature health impact score of the plurality of health impact scores may be based at least in part on the temperature output data measurement;where a carbon dioxide health impact score of the plurality of health impact scores may be based at least in part on the carbon dioxide output data measurement;where a carbon monoxide health impact score of the plurality of health impact scores may be based at least in part on the carbon monoxide output data measurement;where a nitrous dioxide health impact score of the plurality of health impact scores may be based at least in part on the nitrous dioxide output data measurement;where a sulfur dioxide health impact score of the plurality of health impact scores may be based at least in part on the sulfur dioxide output data measurement;where a tVOC health impact score of the plurality of health impact scores may be based at least in part on the tVOC output data measurement;where at least one particulate matter health impact score of the plurality of health impact scores may be based at least in part on the at least one particulate matter output data measurement;

computing, by the processor, an overall health impact score in the at least one site location based at least in part on any of the plurality of health impact scores having a lowest health impact score;

determining, by the processor, at least one verified environmental hazard type in the at least one site location when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter;

generating, by the processor, by at least one machine learning model, at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores into the at least one machine recommendation learning model;

transmitting, by the processor, over the communication network, at least one of:(A) at least one first instruction to display on a computing device at least one of:(i) the overall health impact score,(ii) the at least one verified environmental hazard type, or(iii) the at least one recommendation, or

(B) at least one second instruction to at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.

2. The method according to clause 1, where the computing device may be located inside the at least one site location.

3. The method according to clause 1, where the computing device may be located outside the at least one site location.

4. The method according to clause 1, where the at least one site location may include a room;

where the at least one environment-controlling equipment may include a computer-controlled actuator; and

wherein the transmitting of the at least one second instruction may trigger the computer-controlled actuator to automatically: increase or decrease outdoor air fraction in the room, disperse indoor air in the room, raise or lower a temperature of the room, raise or lower humidity in the room, activate or deactivate air cleaners in the room, or any combination thereof.

5. The method according to clause 1, where the at least one environment-controlling equipment may include at least one electronic circuitry in at least one communication device associated with at least one user; and

where the transmitting of the at least one second instruction may trigger the at least one electronic circuitry in the at least one communication device to alert the at least one user.

6. The method according to clause 1, where the at least one site location may include at least one room in a building;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device in the building; and

where the transmitting of the at least one second instruction may trigger the electronic circuitry in the central computing device in the building to generate an audible alarm, a visual alert, an electronic alert message, or any combination thereof.

7. The method according to clause 1, where the at least one site location may include at least one room in at least one building of an entity;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device at a predefined control-center location of the entity; and

where the transmitting of the at least one second instruction may trigger the electronic circuitry in the central computing device to generate an audible alarm, a visual alert, an electronic alert message, or any combination thereof.

8. The method according to clause 1, where the at least one site location may include at least one room in a building and an outside of the building;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device in the building, and

further including determining, by the processor, that the overall health impact score outside of the building is greater than the overall health impact score inside the at least one room; and

causing, by the processor, over the communication network, to automatically open a window in the at least one room by the transmitting of the at least one second instruction to trigger a computer-controlled window actuator to automatically open the window.

9. The method according to clause 1, where the health impact scoring algorithm may be based at least in part on using a value from each of the plurality of environmental output data measurements and a square of the value.

10. The method according to clause 1, further including storing, by the processor, in a memory, the overall health impact score, the plurality of health impact scores, the at least one verified environmental hazard type, the at least one recommendation, or any combination thereof, that were measured at predefined time intervals.
11. The method according to clause 10, where the transmitting of the first instructions may cause the display to display a history of the overall health impact score, the plurality of health impact scores, the at least one verified environmental hazard type, the at least one recommendation, or any combination thereof, that were measured at the predefined time intervals.
12. The method according to clause 1, where the at least one particulate matter sensor may include a PM10particulate matter sensor, a PM2.5particulate matter sensor, and a PM1particulate matter sensor that respectively output a PM2.5particulate matter output data measurement, a PM2.5particulate matter output data measurement, and a PM1particulate matter output data measurement; and where the executing an health impact scoring algorithm to compute the at least one particulate matter health impact score is based at least in part on the PM2.5particulate matter output data measurement, the PM2.5particulate matter output data measurement, and the PM1particulate matter output data measurement.
13. A system may include a memory and a processor. The processor may be configured to execute computer code stored in the memory that causes the processor to:

receive over a communication network, from a plurality of environmental sensors, a plurality of environmental output data measurements of a plurality of environmental parameters;where the plurality of environmental sensors may be located inside of at least one site location, outside of the at least one site location, or both;where the plurality of environmental sensors may include:(i) an ozone sensor, outputting an ozone output data measurement of the plurality of environmental output data measurements,(ii) a humidity sensor, outputting a humidity output data measurement of the plurality of environmental output data measurements,(iii) a temperature sensor, outputting a temperature output data measurement of the plurality of environmental output data measurements,(iv) a carbon dioxide sensor, outputting a carbon dioxide output data measurement of the plurality of environmental output data measurements,(v) a carbon monoxide sensor, outputting a carbon monoxide output data measurement of the plurality of environmental output data measurements,(vi) a nitrous dioxide sensor, outputting a nitrous dioxide output data measurement of the plurality of environmental output data measurements,(vii) a sulfur dioxide sensor, outputting a sulfur dioxide output data measurement of the plurality of environmental output data measurements,(viii) a total volatile organic compound (tVOC) sensor, outputting a tVOC output data measurement of the plurality of environmental output data measurements, and(ix) at least one particulate matter sensor, outputting at least one particulate matter output data measurement of the plurality of environmental output data measurements;

execute a health impact scoring algorithm software to compute a plurality of health impact scores based at least in part on the plurality of environmental output data measurements from the plurality of environmental sensors;where a humidity health impact score of the plurality of health impact scores may be based at least in part on the humidity output data measurement;where an ozone health impact score of the plurality of health impact scores may be based at least in part on the ozone output data measurement;where a temperature health impact score of the plurality of health impact scores may be based at least in part on the temperature output data measurement;where a carbon dioxide health impact score of the plurality of health impact scores may be based at least in part on the carbon dioxide output data measurement;where a carbon monoxide health impact score of the plurality of health impact scores may be based at least in part on the carbon monoxide output data measurement;where a nitrous dioxide health impact score of the plurality of health impact scores may be based at least in part on the nitrous dioxide output data measurement;where a sulfur dioxide health impact score of the plurality of health impact scores may be based at least in part on the sulfur dioxide output data measurement;where a tVOC health impact score of the plurality of health impact scores may be based at least in part on the tVOC output data measurement;where at least one particulate matter health impact score of the plurality of health impact scores may be based at least in part on the at least one particulate matter output data measurement;

compute an overall health impact score in the at least one site location based at least in part on any of the plurality of health impact scores having a lowest health impact score;

determine at least one verified environmental hazard type in the at least one site location when at least one particular health impact score of the plurality of health impact scores is less than a respective predefined threshold score that is unique to the at least one particular environmental parameter;

generate by at least one machine learning model, at least one recommendation for remediating the at least one verified environmental hazard type when inputting the plurality of health impact scores into the at least one machine learning model;

transmit over the communication network, at least one of:(A) at least one first instruction to display on a computing device at least one of:(i) the overall health impact score,(ii) the at least one verified environmental hazard type, or(iii) the at least one recommendation, or(B) at least one second instruction to at least one environment-controlling equipment located at the at least one site location so as to change an operational parameter of the at least one environment-controlling equipment to mitigate the at least one verified environmental hazard type.
14. The system according to clause 13, where the computing device may be located inside the at least one site location.
15. The system according to clause 13, where the computing device may be located outside the at least one site location.
16. The system according to clause 13, where the at least one site location may include a room;

where the at least one environment-controlling equipment may include a computer-controlled actuator; and

wherein the processor may be configured to transmit the at least one second instruction that triggers the computer-controlled actuator to automatically: increase or decrease outdoor air fraction in the room, disperse indoor air in the room, raise or lower a temperature of the room, raise or lower humidity in the room, activate or deactivate air cleaners in the room, or any combination thereof.

17. The system according to clause 13, where the at least one environment-controlling equipment may include at least one electronic circuitry in at least one communication device associated with at least one user; and

where the processor may be configured to transmit the at least one second instruction that triggers the at least one electronic circuitry in the at least one communication device to alert the at least one user.

18. The system according to clause 13, where the at least one site location may include at least one room in a building;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device in the building; and

where the processor may be configured to transmit the at least one second instruction that triggers the electronic circuitry in the central computing device in the building to generate an audible alarm, a visual alert, an electronic alert message, or any combination thereof.

19. The system according to clause 13, where the at least one site location may include at least one room in at least one building of an entity;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device at a predefined control-center location of the entity; and

where the processor may be configured to transmit the at least one second instruction that triggers the electronic circuitry in the central computing device to generate an audible alarm, a visual alert, an electronic alert message, or any combination thereof.

20. The system according to clause 13, where the at least one site location may include at least one room in a building and an outside of the building;

where the at least one environment-controlling equipment may include electronic circuitry in a central computing device in the building, and

where the processor may be further configured to determine that the overall health impact score outside of the building is greater than the overall health impact score inside the at least one room, and

to cause over the communication network, to automatically open a window in the at least one room by the transmitting of the at least one second instruction to trigger a computer-controlled window actuator to automatically open the window.

21. The system according to clause 13, where the health impact scoring algorithm may be based at least in part on using a value from each of the plurality of environmental output data measurements and a square of the value.

22. The system according to clause 13, where the processor may be further configured to store in the memory: the overall health impact score, the plurality of health impact scores, the at least one verified environmental hazard type, the at least one recommendation, or any combination thereof, that were measured at predefined time intervals.
23. The system according to clause 22, where the processor may be configured to transmit the first instructions that causes the display to display a history of the overall health impact score, the plurality of health impact scores, the at least one verified environmental hazard type, the at least one recommendation, or any combination thereof, that were measured at the predefined time intervals.
24. The system according to clause 13, where the at least one particulate matter sensor may include a PM10particulate matter sensor, a PM2.5particulate matter sensor, and a PM1particulate matter sensor that respectively output a PM2.5particulate matter output data measurement, a PM2.5particulate matter output data measurement, and a PM1particulate matter output data measurement; and where the processor may be configured to execute an health impact scoring algorithm that computes the at least one particulate matter health impact score based at least in part on the PM2.5particulate matter output data measurement, the PM2.5particulate matter output data measurement, and the PM1particulate matter output data measurement.