SYSTEM AND METHOD FOR MITIGATING AIRBORNE CONTAMINATION IN CONDITIONED INDOOR ENVIRONMENTS

A system and method for mitigating airborne contamination in a conditioned indoor environment utilizes one or more sensing modules configured to detect presence and/or concentration of particles and/or aerosols at different locations. A control module employs an artificial intelligence algorithm to selectively activate at least one mitigation module utilizing machine learning programmed rules and output signals from the sensing module(s). The mitigation module(s) are configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment.

BACKGROUND

Airborne diseases are transmitted by the spread of microorganisms (also referred to as microbes) mainly through aerosols and micro droplets. Contaminated micro droplets are frequently generated by an infected host through sneezing, coughing, breathing, speaking, and sweating. Airborne diseases not only affect human health but also detrimentally impact the global economy. Although most efforts are targeted towards protecting individuals from getting infected (e.g., using of personal protective equipment (PPE)), it is also important to promote the maintenance of clean and controlled indoor environments to mitigate airborne contamination and reduce the spread of communicable airborne diseases.

Aerosols are microscopic particles of 0.01 μm to 100 μm in size suspended in air. Ninety-nine percent of aerosols produced by humans (regardless of age, sex, weight, and height) are less than 10 μm. The small size of most aerosols produced by humans is concerning, since smaller aerosols take longer to settle than larger ones and are therefore more likely to be inhaled into the lungs of other individuals. In a turbulent atmosphere, aerosols of 100 μm take an average of 5.8 seconds to settle on surfaces, while 0.5 μm aerosols may take 41 hours to settle. If aerosols contain viable pathogens, they can be a threat while airborne and even after they settle on surfaces since they can generate elements that are sources of contamination. In case of SARS-CoV-2, viruses can be viable on a surface for up to two days.

Although ventilating spaces with fresh air may reduce the concentration of aerosols in indoor environments, introducing large amounts of fresh air may render it difficult to maintain comfortable conditions (e.g., with respect to temperature, humidity, etc.) without expending undue amounts of energy that would increase operating costs and may lead to concomitantly increased carbon emissions.

The art continues to seek improvement in systems and methods for mitigating airborne contamination in conditioned indoor environments, particularly in a manner that does not involve undue expenditure of energy.

SUMMARY

The present disclosure relates to a system and method for mitigating airborne contamination in a conditioned indoor environment. Multiple sensing modules are configured to detect presence and/or concentration of particles and/or aerosols at different locations. A control module employing an artificial intelligence algorithm configured to selectively activate at least one mitigation module based on utilization of machine learning programmed rules and output signals from one or more sensing modules. The at least one mitigation module is configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment.

In one aspect, the disclosure relates to a system for mitigating airborne contamination in a conditioned indoor environment. The system comprises: at least one sensing module that comprises an aerosol and/or particulate detector configured to detect presence and/or concentration of particles and/or aerosols at a location in the conditioned indoor environment; at least one mitigation module configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment; and a control module employing an artificial intelligence algorithm configured to selectively activate the at least one mitigation module based on utilization of machine learning programmed rules and output signals from one or more sensing modules of the at least one sensing module.

In certain embodiments, the system further comprises at least one sampling module configured to automatically collect an air sample from the conditioned indoor environment based on utilization of machine learning programmed rules and output signals from one or more modules of the at least one sensing module.

In certain embodiments, the at least one sampling module comprises at least one of a chemical analyzer or a biological analyzer configured to identify one or more constituents of the air sample.

In certain embodiments, the at least one sensing module comprises at least one of a temperature sensor, a pressure sensor, a carbon dioxide sensor, and a humidity sensor.

In certain embodiments, each sensing module of the at least one sensing module comprises an occupancy sensor configured to sense the presence of at least one human within the conditioned indoor environment.

In certain embodiments, the at least one mitigation module comprises a ventilation module configured to increase exchange of air between the conditioned indoor environment and an outdoor environment, wherein the ventilation module comprises an inlet fan and an outlet fan.

In certain embodiments, the at least one mitigation module comprises at least one of a wet scrubber or a dry scrubber.

In certain embodiments, the at least one mitigation module comprises a disinfection module configured to disinfect air of the conditioned indoor environment.

In certain embodiments, the disinfection module comprises at least one of an ozone generator or an ultraviolet lamp.

In certain embodiments, the at least one mitigation module comprises a filtration module.

In certain embodiments, the at least one mitigation module comprises a plurality of mitigation modules of different types.

In certain embodiments, at least a portion of the at least one mitigation module is positioned in ductwork of an HVAC apparatus associated with the conditioned indoor environment.

In certain embodiments, the system further comprises a reporting module configured to receive at least one signal from the control module, and responsively generate a user-perceptible alarm signal.

In certain embodiments, the system further comprises a reporting module configured to (i) receive at least one signal from the control module, (ii) store information indicative of or derived from the at least one signal, and (ii) generate one or more reports comprising information indicative of or derived from the at least one signal.

In certain embodiments, the at least one sensing module comprises a plurality of sensing modules.

In certain embodiments, the control module is further configured to control a HVAC apparatus associated with the conditioned indoor environment.

In another aspect, the disclosure relates to a method for mitigating airborne contamination in a conditioned indoor environment. The method comprises: detecting presence and/or concentration of particles and/or aerosols at a location in the conditioned indoor environment using at least one sensing module, wherein each sensing module of the at least one sensing module comprises an aerosol and/or particulate detector; and utilizing a control module employing an artificial intelligence algorithm to selectively activate, based on utilization of machine learning programmed rules and output signals from the at least one sensing module, at least one mitigation module configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment.

In certain embodiments, the method further comprises automatically collecting an air sample from the conditioned indoor environment, using at least one sampling module, based on utilization of machine learning programmed rules and output signals from one or more modules of the at least one sensing module.

In certain embodiments, the method further comprises identifying one or more constituents of the air sample using at least one of a chemical analyzer or a biological analyzer associated with the at least one sampling module.

In certain embodiments, the at least one mitigation module comprises a ventilation module configured to increase exchange of air between the conditioned indoor environment and an outdoor environment, the ventilation module comprising an inlet fan and an outlet fan; and activating the ventilation module comprises using the outlet fan to exhaust at least a portion of air received from the conditioned indoor environment to an external environment, and comprises using the inlet fan to draw air from the external environment to the conditioned indoor environment.

In certain embodiments, the at least one mitigation module comprises a dry scrubber, and activating the dry scrubber comprises directing an air stream received from the conditioned indoor environment to contact one or more dry reagents configured to interact with constituents of the air stream.

In certain embodiments, the at least one mitigation module comprises a wet scrubber, and activating the wet scrubber comprises directing an air stream received from the conditioned indoor environment to contact one or more liquid reagents configured to interact with constituents of the air stream.

In certain embodiments, the at least one mitigation module comprises a disinfection module, and activating the disinfection module comprises activating at least one of an ozone generator or an ultraviolet lamp of the disinfection module.

In certain embodiments, the at least one mitigation module comprises a filtration module that includes a filter and a diverter, and activating the filtration module comprises activating the diverter to direct an air stream received from the conditioned indoor environment to pass through the filter.

In certain embodiments, the at least one mitigation module comprises a plurality of serially arranged mitigation modules of different types.

In certain embodiments, at least a portion of the at least one mitigation module is positioned in ductwork of an HVAC apparatus associated with the conditioned indoor environment.

In certain embodiments, the method further comprises receiving at least one signal from the control module, and responsively generating a user-perceptible alarm signal based on comparison of the at least one signal to at least one predetermined threshold value.

In certain embodiments, the method further comprises using a reporting module to receive at least one signal from the control module, to store information indicative of or derived from the at least one signal, and to generate one or more reports comprising information indicative of or derived from the at least one signal.

In certain embodiments, the at least one sensing module comprises a plurality of sensing modules.

In another aspect, the disclosure relates to a non-transitory computer readable medium containing program instructions for receiving signals from at least one sensing modules and for controlling operation of at least one mitigation module configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in a conditioned indoor environment, to perform a method comprising: detecting presence and/or concentration of particles and/or aerosols at a location in the conditioned indoor environment using at least one sensing module that comprises an aerosol and/or particulate detector; and utilizing a control module employing an artificial intelligence algorithm to selectively activate, based on utilization of machine learning programmed rules and output signals from the at least one sensing module, the at least one mitigation module configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment.

In a further aspect, any aspects, embodiments, or other features described herein may be combined for additional advantage.

DETAILED DESCRIPTION

As introduced previously, a system and a method for mitigating airborne contamination in a conditioned indoor environment are provided herein. Multiple sensing modules are configured to detect presence and/or concentration of particles and/or aerosols at different locations. A control module employing an artificial intelligence algorithm configured to selectively activate at least one mitigation module based on utilization of machine learning programmed rules and output signals from one or more sensing modules. The at least one mitigation module is configured to take one or more actions to reduce presence and/or concentration of particles and/or aerosols in the conditioned indoor environment. The use of an artificial intelligence algorithm employing machine learning enables mitigating actions to be taken at particularly relevant times to reduce the proliferation of microbes in an interior environment, preferably in a manner to avoid undue expenditure of energy. An interior environment may be controlled based on metrics relevant to the spread of diseases by aerosols and particulates. Mitigation measures may be utilized only when and where necessary, so that mitigation measures that consume energy are not operated on a continuous basis.

Systems and methods disclosed herein may be utilized in various structures and contexts, such as classrooms, hospitals and health-related offices and clinics, public places, government buildings, industrial buildings, airports, transportation, facilities in rural areas, retail stores, corporate facilities, and the like. In certain embodiments, wireless communication may be used between sensing modules, mitigation modules, and a control module.

FIG. 1is an illustrative diagram showing how droplets and aerosols may be propagated from an infected host10(e.g., a SARS-CoV-2 infected host) to a susceptible host12in an environment, and how environmentally stable fomites23derived from droplets14and/or aerosols16may accumulate within surfaces of the environment.FIG. 1is adapted from the following source: T. Galbadage, B. M Peterson, R. S. Gunasekera, Does COVID-19 spread thorugh droplets alone? Front. Public Health, 24 Apr. 2020, available online at URL:<https://doi.org/10.3389/fpubh.2020.00163>.

FIG. 2is a plot of particle concentration versus time showing particle counts sensed at three different distances (3, 6, and 9 feet, respectively) from a patient undergoing nebulizer therapy. Nebulization treatments are targeted to provide a mean of treatment for a respiratory disease. This treatment causes a high risk of spreading pathogens due to the nature of the therapy. A problem is the aerosol portions that do not reach the alveolar area, remain in the dead volume of the respiratory system (including nose and mouth) in contact with infectious areas, and are exhaled into the environment as contaminated aerosols. The “nebulizer on” portion ofFIG. 2shows particle count concentration during nebulization treatment of a patient under oxygen therapy using a high flow nasal cannula with 21% Oxygen at 30 L/min and nebulization with 3 ml of medication in a saline physiological solution. Comparative particle count profiles are illustrated for a COVID-19 infected patient at positions of 3 feet, 6 feet, and 9 feet from the subject, with the subject not wearing any mitigation mask. The spread of potentially contaminated aerosol is evident, and unpredictable. For example, aerosol concentration at a distance of 9 feet is larger than at a distance of 6 feet.

FIG. 3is a schematic diagram illustrating elements of a system20for mitigating airborne contamination in a conditioned indoor environment according to one embodiment. A sensing module24is illustrated at lower left, with the sensing module24including a circuit board26having mounted thereon a microcontroller (or CPU)28, and with the circuit board26having various sensors (e.g., relative humidity sensor30, temperature sensor32, barometric pressure sensor34, etc.) mounted thereon, and additional sensors (e.g., carbon dioxide and particle sensors) coupled to the circuit board24via input/output ports24. Various types of sensors that may be used include, but are not limited to, one or more particle/aerosol sensors (optionally configured to detect particles/aerosols of different sizes, such as particles/aerosols 0.2 μm or larger (e.g., 0.2 μm to 2.0 μm size range), and particles/aerosols 2.0 μm or larger (e.g., 2.0 μm to 10.0 μm size), temperature sensor, barometric pressure sensor, humidity sensor, carbon dioxide sensor, and any other sensors that may be used to detect occupancy of the environment or conditions indicative of indoor air quality and/or comfort of humans in a conditioned space where the sensing module is positioned. At least a portion of a mitigation module40is illustrated at lower right, with the illustrated mitigation module40comprising a ventilation module that is configured to increase exchange of air between the conditioned indoor environment and an outdoor environment, wherein the ventilation module40is configured to control (and optionally comprises) an inlet fan and an outlet fan. The ventilation module40may be used to increase a rate of air exchange between an indoor conditioned environment and an external environment in order to decrease a particle/aerosol level in the conditioned environment to a baseline level, responsive to detection by the sensing module24of particle/aerosol levels above a normal baseline level. The ventilation module40(as an example of a mitigation module) may include a circuit board42, a microcontroller (or CPU)44, power converters46, relays48, and electrical connectors50for fans (e.g., an inlet fan and an outlet fan), compressors, dampers, diverters, and/or other HVAC components. The sensing module24and the ventilation module40are arranged to communicate, either wirelessly (e.g., via Bluetooth) or by wired means, with a control module22that includes a microprocessor (or CPU) and memory (not shown), with the control module22employing an artificial intelligence (AI) algorithm configured to selectively activate the at least one mitigation (i.e., ventilation) module40. The control module22and AI algorithm may utilize machine learning programmed rules as well as input signals from one or more sensing modules24to control operation of the mitigation module40.

FIG. 4is a schematic diagram illustrating elements of a system60for mitigating airborne contamination in a conditioned indoor environment or space62according to one embodiment. The system60includes a control module64having a processor66(e.g., a microprocessor such as a CPU), a memory68, and a communication element69(e.g., Bluetooth or similar) that is operatively coupled with multiple sensing modules70A-70N (i.e., sensing modules A to N, where N represents any suitable number), multiple mitigation modules80,90,100,110,120, a reporting module140, a sampling module78, and a HVAC apparatus130. Each sensing module70A-70N is arranged in an indoor conditioned space62, and may include multiple sensors71A-76A,71B-76B,71N-76N. Various sensors that may be employed in each sensing module70A-70N may include one or more particle/aerosol sensors71A-71N (optionally including multiple particle/aerosol sensors), temperature sensors72A-72N, barometric pressure sensors73A-73N, humidity sensors75A-75N, carbon dioxide sensors74A-74N, and any other sensors that may be used to detect conditions indicative of indoor air quality and/or comfort of humans in the conditioned indoor space62. The sensing modules70A-70N may be arranged at different locations in the conditioned space62. Alternatively, a single sensing module70A may be positioned in the conditioned space, and may communicate with multiple aerosol/particle sensors that can be located at different locations in the conditioned space62. The conditioned space62includes a duct loop150having at least one air supply duct152and at least one return air duct154that are coupled with a HVAC apparatus130, wherein the HVAC apparatus130includes a fan132, a compressor134, a heat exchanger136, and one or more dampers138. At least portions of the sampling module78and the various mitigation modules80,90,100,110,120may be arranged in or proximate to the duct loop150. The sampling module78may be arranged to automatically collect an air sample from an air stream received from the conditioned indoor environment62(e.g., via return air duct154) based on a control signal received from the control module64, with such control signal utilizing of machine learning programmed rules and output signals from one or more of the plurality of sensing modules70A-70N. The sampling module78may be used for automatically gathering samples at critical moments of higher aerosol/particulate concentration, and may provide speciation of aerosols/particulates. In certain embodiments, the sampling module78may include a chemical analyzer and/or a biological analyzer (e.g., including but not limited to a specific binding assay device) configured to identify one or more constituents of a collected air sample. In certain embodiments, samples gathered by the sampling module78may be analyzed at an offsite facility (not shown).

The various mitigation modules80,90,100,110,120shown inFIG. 4include a dry scrubber module80, a disinfection module90, a wet scrubber module, a filtration module, and a ventilation module. In certain embodiments, a mitigation module may include a diverter, which may include one or more dampers or other air redirecting devices that serve to direct some or all of an air stream from a primary duct loop to a secondary duct section associated with the mitigation module. Any one or more of the mitigation modules may be provided, and controlled by the control module utilizing machine learning programmed rules as well as input signals from one or more of the sensing modules. For example, if a condition indicative of high aerosol or particulate concentration in the conditioned space is identified, the control module may activate one or more of the mitigation modules in order to reduce a concentration of aerosols or particulates in the conditioned space. In certain embodiments, activating the dry scrubber comprises directing an air stream received from the conditioned indoor environment to contact one or more dry reagents configured to interact with constituents of the air stream. In certain embodiments, activating the disinfection module comprises activating at least one of an ozone generator or an ultraviolet lamp of the disinfection module, possibly in conjunction with operating a diverter of the disinfection module. In certain embodiments, activating the wet scrubber module comprises activating a diverter, a liquid reagent pump, and a dryer, to cause at least a portion of an air stream to interact with a liquid reagent followed by drying of a wetted air stream to reduce concentration of aerosols or particulates. In certain embodiments, activating the filtration module comprises activating a diverter to direct at least a portion of an air stream from a primary loop to a secondary loop containing a high efficiency filter (e.g., a HEPA filter or bacterial/viral filter), optionally in conjunction with activating a filtration fan to force diverted air through the high efficiency filter. In certain embodiments, activating the ventilation module comprises operating an outlet fan to exhaust at least a portion of air received from the conditioned indoor environment to an external environment, and comprises operating an inlet fan to draw air from the external environment to the conditioned indoor environment, optionally in conjunction with operating a diverter or other airflow control apparatus to prevent an air stream from bypassing the inlet and outlet fans or a scrubber filtering system9e.g., bacterial/viral filter, activated carbon filter, etc.) to ensure the entrance of clean air. The reporting module may include a memory and a communication element, optionally in conjunction with a display. In certain embodiments, the reporting module is configured to receive at least one signal from the control module, and responsively generate a user-perceptible alarm signal. In certain embodiments, the reporting module is configured to (i) receive at least one signal from the control module, (ii) store information indicative of or derived from the at least one signal, and (ii) generate one or more reports comprising information indicative of or derived from the at least one signal. The reports can be audible (e.g., alarms), visual (e.g., color coded displayed signals), and/or tactile (e.g., vibration).

In certain embodiments, an AI algorithm utilized by a control module comprises a neural network algorithm inspired by biological neurons. A deep neural network may utilize many layers of connected neurons in sequence. An exemplary neural network may include an input layer, one or more hidden layers, and an output layer.

In certain embodiments, an AI algorithm utilized by a control module employs machine learning, which may include supervised, unsupervised, semi-supervised, and/or reinforcement learning. An AI algorithm built with machine learning may be generated by providing prepared training date to an AI algorithm. Such a process may include gathering raw data, preparing training data, training and optimizing an AI model, integrating an AI model, testing/evaluating an AI model, and placing an AI algorithm (obtained from the AI model) in operational use. In certain embodiments, data obtained through operational use of an AI algorithm may be used to prepare additional training data for further refinement and/or updating of the AI algorithm.

FIG. 5is a schematic diagram of components of a system160for mitigating airborne contamination in a conditioned indoor environment, including components used for generating and updating an artificial intelligence algorithm employed by a control module. The system160includes sensing modules170, a control module64, and one or more mitigation modules120. The sensing modules170provide operational input data162to the control module64, which operates an AI algorithm164that may be implemented in AI software. Through operation of the AI algorithm164, the control module64provides operational output data166(e.g., control signals) to one or more mitigation modules120. Although not shown inFIG. 5, the control module64may also provide signals to a reporting module, a sampling module, and/or a HVAC apparatus (e.g., as depicted inFIG. 4). To generate an AI algorithm164, training data172(e.g., input and output data) may be supplied to a machine learning training algorithm174to produce a trained AI model176. After sufficient machine learning training is complete (and any desired testing and validation is completed), the trained AI model176may be placed into operational use as the AI algorithm164used by the control module64. In certain embodiments, data obtained during operational use of the AI algorithm164(e.g., operational input data162generated by the sensing modules170, and operational output data166provided to the mitigation modules120) may be used to prepare additional training data for further refinement and/or updating of the AI algorithm164employed by the control module64.

FIG. 6is a diagram showing placement of sensing modules170A-170C having particle sensors (e.g., counters) at different distances (i.e., 3, 6, and 13 feet, respectively) from a patient178undergoing nebulizer therapy via a nebulizer182incorporating oxygen delivery184, wherein the patient178may have associated therewith exposure mitigation equipment186such as a mask, filter, and/or limited air exchange apparatus. Sensing modules170A-170C are desirably placed at different locations in a conditioned environment180, since aerosol and/or particulate concentration may vary considerably within the conditioned environment180due to flows of air generated by a HVAC system.

FIG. 7illustrates a first graphical user interface (GUI)190of a computing device (e.g., tablet computer) that may serve as a reporting module, with the computing device including a memory for storing data and a display that provides recorded values (e.g., plotted with respect to time) and instantaneous values for outputs of particle/aerosol sensors of three different recording modules. For example, an application may collect and display 0.2+ μm and 2+ μm particle/aerosol levels over various timeframes, such as hourly, 24 hours, weekly, monthly, etc. The first GUI190includes instantaneous readings191A-191C for three particle sensors arranged at different distances (i.e., 3, 6, and 13 feet, respectively) from a patient, and further includes time-varying plots192A-192C for outputs of these sensors. The first GUI190additionally includes an equipment identification window193(showing associated motor and sensor identifiers), a stop test button194, an export data (export CSV) button195, and a setting status window196.

FIG. 8illustrates a second GUI for the computing device referenced inFIG. 7, showing user-settable aerosol/particulate threshold windows198A-198C (for particle counts obtained by sensors distanced 3 feet, 6 feet, and 12 feet from a patient or location of interest), an alarm threshold window199, and a keyboard window201. Any one or more of the high thresholds in windows198A-198C,199may be utilized for trigger operation of fans of a ventilation module of a system for mitigating airborne contamination according to one embodiment.

FIG. 9illustrates a third GUI202for the computing device referenced inFIG. 7, showing instantaneous particle sensing windows191A-191B that may be shaded or colored to provide visual signals (optionally supplemented with audible signals) if a particle count threshold is attained by sensing with one or more sensing modules corresponding to levels of aerosols/particulates higher than one or more predetermined baseline or threshold values. The third GUI further includes threshold status windows204that may be used to identify currently set high and low threshold values.

FIG. 10is a plot of particle count per cubic feet versus time sensed by a sensing module with three aerosol/particulate sensors each including discrete capability for sensing 0.2+ μm (e.g., 0.2 μm-2.0 μm size range) and 2+ μm (e.g., 2.0 μm to 10.0 μm size range) particle/aerosol levels. As shown, spikes in detected 0.2+ μm particles and 2+ μm particles around 23:20:00 triggers operation of a ventilation fan of a ventilation module, in order to promote exchange air between a conditioned indoor environment and an outdoor environment, in order to reduce concentration of aerosols/particles in the conditioned indoor environment. When the detected concentration of aerosols/particles returns to acceptable levels, operation of the ventilation module may be discontinued as unnecessary, until another spike in aerosols/particles is detected.

FIG. 11provides a plot of three comfort parameters (temperature (F)), temperature (C), and relative humidity) sensed by one or more sensing modules for the indoor environment in a timeframe overlapping the timeframe plotted inFIG. 10. As shown, temperature and relative humidity values remain stable over the displayed time period.

FIG. 12is a plot of carbon dioxide concentration sensed by one or more sensing modules for the indoor environment in a timeframe overlapping the timeframe plotted inFIG. 10. As shown, carbon dioxide concentration values remain stable over the displayed time period.

FIG. 13illustrates a fourth GUI210including a plot212of carbon dioxide concentration in a conditioned indoor environment, with additional windows214providing instantaneous readings of comfort parameter values (temperature, humidity, barometric pressure, and carbon dioxide concentration) obtained from a sensing module for a test performed in an office environment. The carbon dioxide levels analysis may be associated to an AI algorithm measuring the metabolic rate (kcal/day) of a sole occupant in the environment.

FIGS. 14A-14Crepresent portions of a GUI220useable with a computing device connected to sensing modules described herein, with each figure including instantaneous reading windows221A-221C and time-varying plots222A-222C of particle/aerosol concentration of two different size thresholds (0.5+ μm particles and 2.5+ μm particles) obtained with first through third sensing modules, respectively, during the test represented inFIG. 13.FIG. 14Aprovides values for a sensing module positioned 3 feet from a patient or location of interest,FIG. 14Bprovides values for a sensing module positioned 6 feet from the patient or location of interest, andFIG. 14Cprovides values for a sensing module positioned 13 feet from the patient or location of interest, Such figures show the triggering of a ventilation module at two time periods (proximate to time=0 and time=117 minutes) responsive to detection of elevated particulate/aerosol concentration values.

FIG. 14Drepresents an additional portion of the graphical user interface220supplementing the portions shown inFIGS. 14A-14C, including a control and threshold/alarm identification window224, and additional windows216providing instantaneous readings of comfort parameter values (temperature, humidity, barometric pressure, and carbon dioxide concentration).

FIGS. 15A-15Cillustrate placement of sensing modules at different locations in a bathroom.FIG. 15Ashows a first sensing module170A arranged in a hallway proximate to a door230and floor234(and adjacent to a wall232A) of a bathroom as an example of one location for sensing module placement.FIG. 15Bshows a second sensing module1708mounted to a partition236B near shoulder level proximate to a urinal238(located between the partition236B and an opposing wall232B, and elevated above a floor234) in the bathroom as another example of a location for sensing module placement.FIG. 15Cshows a third sensing module170C mounted to a partition236C (elevated above a floor234) and near waist level proximate to a toilet240in a stall (having a door242) in the bathroom as another example of a location for sensing module placement. The different placement of the sensing modules170A-70C inFIGS. 15A-15Cis expected to yield different sensed values for particulate/aerosol concentration in the same room.

FIGS. 16A-16Crepresent portions of a GUI230useable with a computing device connected to sensing modules described herein, with each figure including instantaneous reading windows221A-221C and time-varying plots232A-232C of particle/aerosol concentration of two different size thresholds (0.5+ μm particles and 2.5+ μm particles) obtained with first through third sensing modules170A-170C ofFIGS. 15A-15C.FIG. 14Aprovides values for a sensing module positioned 3 feet from a location of interest,FIG. 14Bprovides values for a sensing module positioned 6 feet from a location of interest, andFIG. 14Cprovides values for a sensing module positioned 13 feet from a location of interest.FIGS. 14A-14Cshow detected spikes in aerosol/particulate concentration at different times, corresponding to events such as urination in the urinal (seeFIG. 16B), fecal matter flushing in the toilet (seeFIG. 16C), and urine flushing in the toilet (seeFIG. 16C), wherein operation of a ventilation module is triggered at two time periods responsive to detection of elevated particulate/aerosol concentration values. The first sensing module (inFIG. 16A) shows particle/aerosol concentration values below a baseline (18,000 particles/ft3) at all times. The second sensing module (inFIG. 16B) also shows particle/aerosol concentration values below a baseline (18,000 particles/ft3) at all times, demonstrating that urination in a chemical urinal does not produce unduly high aerosol concentrations. The third sensing module (inFIG. 16C) shows spikes in particle/aerosol concentration every time the toilet is flushed, and values clearly above the baseline when flushing is associated with disposal of fecal matter. In view of the foregoing, in certain embodiments, an AI algorithm receiving data from sensing modules in a bathroom environment may be used to responsively and/or prophylactically initiate one or more mitigation modules when a fecal matter flushing event is detected or is considered to be imminent (e.g., by detection of a condition indicative of an occupant seated on a toilet, or other conditions).

FIG. 17Ais a plot of aerosol/particulate concentration versus time obtained by a sensing module of a system as disclosed herein, taken over a period of 54 hours and including a baseline value threshold region (i.e., for concentration values below 500000). As shown, three time regions exceed the baseline region, with the latter time region (on Jun. 28, 2020) exhibiting the highest overage suitable for triggering a sampling period.

FIG. 17Bshows the plot ofFIG. 17A, with identification of sampling periods triggered by sensing (by one or more sensing modules) of aerosol/particulate concentration values above a baseline value. Four sampling periods (windows) are shown. A sampling module may be used for gathering samples at critical moments of higher aerosol/particulate concentration, and may provide speciation of aerosols/particulates. In certain embodiments, a sampling module may include a chemical analyzer and/or a biological analyzer (e.g., including but not limited to a specific binding assay device) configured to identify one or more constituents of a collected air sample.

FIG. 18is a diagram showing steps using a biological sensor240to provide speciation of aerosols/particulates. The illustrated biological sensor240comprises an inline urine imaging cytometer (i.e., fluid imaging meter) that utilizes a laser243, a forward scattering CMOS imager244, and a side scattering CMOS imager254to generate images of a fluid sample (e.g., urine) contained in a sample channel242, within such image generation involving a first step. A second step involves recording and sequencing the images obtained from the CMOS imagers244,254. A third step includes extracting features from the sequenced images to generate individual particle scattering signals. A fourth step includes suppling the individual particle scattering signals as inputs to a machine learning model that employs multiple hidden layers between an input layer and an output layer. A fifth step includes classifying results obtained from the machine learning model (e.g., counts of particulate elements, and discrimination of type of particle, such as white blood cell, red blood cell, bacteria, crystal, etc.).FIG. 18was adapted from the following source: Rafael Iriya, Wenwen Jing, Karan Syal, Manni Mo, Chao Chen, Hui Yu, Shelley E Haydel, Shaopeng Wang, Nongjian Tao, Rapid antibiotic susceptibility testing based on bacterial motion patterns with long short-term memory neural networks,IEEE Sensors Journal, vol. 20, no. 9, pp. 4940-4950, May 1, 2020. NIHMS1588088.

FIG. 19Ashows components of a first biological sensor250that may be incorporated in a sampling module as disclosed herein, including a sample holder254(e.g., for receiving a urine sample from a via252, plus an optionally added antibiotic) that is arranged between a light slab256and an optical assembly256. Scattered light260produced by the illuminated sample holder254is received by a camera262to produce an output signal.

FIG. 19Bshows components of a second biological sensor270that may be incorporated in a sampling module as disclosed herein, with the sensor270including a sample container274arranged to be illuminated by a laser276emitting through a cylindrical lens278, and including a zoom lens284and an associated camera282arranged orthogonally to the cylindrical lens278to capture images of the sample within the sample container274. A first translation stage280is associated with the cylindrical lens278to adjust illumination of the sample container274, and a second translation stage286is associated with zoom lens284to facilitate imaging using the camera282. A temperature sensor288is additionally provided.FIG. 19Bis adapted from the following source: M Mo, Y Yang, F Zhang, W Jing, R Iriya, J Popovich, S Wang, T Grys, S. E. Haydel, N. Tao, Rapid Antimicrobial Susceptibility Testing of Patient Urine Samples using Large Volume Free-Solution Light Scattering Microscopy,Analytical chemistry,2019, 91 (15), 10164-10171. DOI: 10.1021/acs.analchem.9b02174. PMCID: PMC7003966.

FIG. 20provides four representative frames from videos of mixed bacteria (E. coli) and 0.5 μm polystyrene particles in water from optical sensors shown inFIGS. 18, 19A, and 19B, wherein bacteria cells are highlighted in gray dashed line circles, and particles are highlighted in white dashed line circles, with a scale bar showing a 50 μm scale. A comparison of the four frames shows that the bacteria cells exhibit greater movement (change in position) than the particles.

FIG. 21is a plot of y position versus x position for bacteria and particles in the video represented inFIG. 20, showing trajectories of three bacteria (Bac1 to Bac 3) and three particles (Par1 to Par3).

FIGS. 22A-22Cprovide plots of trajectories (y position versus x position) for the three bacteria (Bac1 to Bac3) represented inFIGS. 20-21, respectively.

FIGS. 23A-23Cprovide plots of intensity (au) versus time for the three bacteria represented inFIGS. 20-22C, respectively.

FIGS. 24A-24Cprovide plots of trajectories (y position versus x position) for the three particles represented inFIGS. 20-21. The particle trajectories shown in FIGS.24A-24C have a smaller positional variation and are significantly different from the bacteria trajectories shown inFIGS. 22A-22C.

FIGS. 25A-25Cprovides plots of intensity (au) versus time for the three particles represented inFIGS. 20, 21, and 24A-24C. The intensity variation magnitude and patterns ofFIGS. 25A-25Cfor particles differ significant from their counterparts shown inFIGS. 23A-23Cfor bacteria.

FIGS. 20-25demonstrate the capacity of optical system such as the one shown inFIGS. 18, 19A, and 19Bto discriminate bacteria from particles based on the imaging sensor signal processing.

FIG. 26shows components of a chemical sensor290that may be incorporated in a sampling module as disclosed herein to detect metabolites of microbes. The chemical sensor290may include a CMOS image chip292or equivalent optical system, which may be used to identify a change in state of the sensor upon exposure to one or more chemical species (e.g., ammonia as illustrated, or others such as phenol p-cresol, indole, hydrogen sulfide, nitrite, nitrate, methane, etc.). The frame at upper right inFIG. 26shows the CMOS image chip292with multiple sensing areas represented in a first state (e.g., color distribution) prior to exposure to ammonia. The frame at lower right inFIG. 26shows the CMOS image chip292′ in a second state, with sensing areas having a different color distribution after exposure to ammonia.FIG. 26was adapted from the following source: Kyle R. Mallires, Di Wang, Peter Wiktor and Nongjian Tao, A Microdroplet-Based Colorimetric Sensing Platform on a CMOS Imager Chip, Anal. Chem. 2020, 92, 9362-9369.

FIG. 27illustrates a gelatin filter impactor300that may be associated with a sampling module, and may be used for collection of aerosols/particles followed by transportation to a remote detector for speciation of any collected aerosols/particles. The gelatin filter impactor300includes a body301having a raised wall302that contains a cavity303configured to hold gelatin or another cell culturing medium. The gelatin filter impactor300additionally includes a lid304having a wall structure305configured to cooperated with the body301and/or raised wall302, and includes filtration media306spanning at least a portion of the lid304. In use, air can be drawn through the filtration media306(e.g., using a vacuum pump applied to the body301or other means) into the gelatin filter impactor300, to permit aerosols and/or particles to contact gelatin within the cavity303so that species within the aerosols and/or particles may be cultured for further analysis. In certain embodiments, the filtration media306may have geometric characteristics (e.g., pore shape, pore size, pore distribution, etc.) selected to promote preferential passage of species of interest into the cavity303.

FIG. 28is a schematic diagram of a generalized representation of a computer system400(optionally embodied in a computing device) that can be utilized as, or included in a component of, a control module as disclosed herein. In this regard, the computer system400is adapted to execute instructions from a computer-readable medium to perform these and/or any of the functions or processing described herein. The computer system400inFIG. 28may include a set of instructions that may be executed to program and configure programmable digital circuits for controlling a system for mitigating airborne contamination of a conditioned indoor environment. The computer system400may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. While only a single device is illustrated, the term “device” shall also be taken to include any collection of devices that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. The computer system400may be a circuit or circuits included in an electronic board card, such as a printed circuit board (PCB), a server, a personal computer, a desktop computer, a laptop computer, a personal digital assistant (PDA), a computing pad, a mobile device, or any other device, and may represent, for example, a server or a user's computer.

The computer system400in this embodiment includes a processing device or processor402, a main memory404(e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM), etc.), and a static memory406(e.g., flash memory, static random access memory (SRAM), etc.), which may communicate with each other via a data bus408. Alternatively, the processing device402may be connected to the main memory404and/or static memory406directly or via some other connectivity means. The processing device402may be a controller, and the main memory404or static memory406may be any type of memory.

The processing device402represents one or more general-purpose processing devices, such as a microprocessor, central processing unit, or the like. More particularly, the processing device402may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or other processors implementing a combination of instruction sets. The processing device402is configured to execute processing logic in instructions for performing the operations and steps discussed herein.

The computer system400may further include a network interface device410. The computer system400also may or may not include an input412, configured to receive input and selections to be communicated to the computer system400when executing instructions. The computer system400also may or may not include an output414, including but not limited to a display, a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device (e.g., a keyboard), and/or a cursor control device (e.g., a mouse).

The computer system400may or may not include a data storage device that includes instructions416stored in a computer readable medium418. The instructions416may also reside, completely or at least partially, within the main memory404and/or within the processing device402during execution thereof by the computer system400, the main memory404and the processing device402also constituting computer readable medium. The instructions416may further be transmitted or received over a network420via the network interface device410.

While the computer readable medium418is shown in an embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the processing device402and that cause the processing device402to perform any one or more of the methodologies of the embodiments disclosed herein. The term “computer readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

Unless specifically stated otherwise and as apparent from the previous discussion, it is appreciated that throughout the description, discussions utilizing terms such as “analyzing,” “processing,” “computing,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or a similar electronic computing device, that manipulates and transforms data and memories represented as physical (electronic) quantities within registers of the computer system into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission, or display devices.

The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatuses to perform the required method steps. The required structure for a variety of these systems is disclosed in the description above. In addition, the embodiments described herein are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the embodiments as described herein.

FIG. 29is a schematic diagram showing components of a system440for detecting and mitigating airborne contamination with continuous monitoring and periodic sampling. The system440includes an airborne particle sensing module442, an artificial intelligence (AI) application (or app)444that may embody a control module implemented in computer hardware and software (in one or more a stationary computing devices and/or mobile computing devices such as a smartphone or tablet computer), a sample collector and sensor module446, and one or more environmental mitigation modules448. According to block450, the airborne particle sensing module442may be used for continuous monitoring of airborne concentrations of small particles (e.g., 0.2-2.0 μm diameter) and larger particles (e.g., 2.0-10 μm diameter). Signals from the airborne particle sensing module442may be supplied to the AI app444. According to block452, the AI app444may be used to set a threshold (e.g., baseline level) for acceptable particulate level and/or specific pathogen level in the environment being monitored. As noted in block454, the AI app444may be used to control the sensing module442, the sample collection/sensor module446, and the environmental mitigation module448. According to block456, a concentration of particles detected by the airborne particle sensing module442is compared to a particulate concentration threshold or baseline. Additionally or alternatively according to block456, a concentration of pathogens detected by the sample collection/sensor module446is compared to a pathogen concentration threshold or baseline. (The sample collection/sensor module446may serve to collect an air sample, preconcentrate one or more pathogens on a preconcentration surface, and sense one or more pathogens such as SARS-SoV-2 on the preconcentration surface, wherein the sample collection and sensor module446may include one or more impactors, such as described in connection withFIGS. 31A-31B.) The preconcentration function preconcentrates the aerosol particles in the air on a surface, which improves the sensitivity of the device and allows for viral antigen measurement in low concentration aerosols. The sensing function detects the presence of a pathogen-specific antigen (e.g., SARS-CoV-2 antigen) on the preconcentration surface, using a highly sensitive probe (e.g., a quantum dot measurement technique) combined with an aptamer that only binds to a target protein of the pathogen (e.g., SARS-CoV-2's N protein), giving the technique extremely high selectivity even to viral matter in the same family (e.g. SARS-CoV-1 and MERS). If the comparison step of block456shows that a detected particle level exceeds a particulate threshold or baseline concentration and/or that a detected pathogen level exceeds a pathogen threshold or baseline concentration, then such comparison(s) may trigger the initiation or continuation of air sampling and pre-concentration (according to block458) followed by sensing of concentration of the target pathogen(s) (according to block460). In certain embodiments, the target pathogen(s) may include SARS-CoV-2. Additionally or alternatively, if the comparison step of block456shows that a detected particle level exceeds a particulate threshold or baseline, then operation of the environmental mitigation module(s)448may be initiated or modified (according to block462) until a particulate level (e.g., sensed by the sensing module442) and/or pathogen level (e.g., sensed by the sampling and sensor module446) returns to at or below the relevant baseline level(s). The environmental mitigation module448may embody any suitable type of mitigation module(s) disclosed herein, including but not limited to filtration, ozone and/or ultraviolet disinfection, dry scrubbing, wet scrubbing, and the like, which may be associated with a HVAC or ventilation apparatus of a particular structure or environment. In certain embodiments, the system442may be referred to as a Transmission Reduction Artificial Intelligence (AI) System or “TRAIS.”

In certain embodiments, one group of steps according to various blocks described above may be performed in an intermittent operational mode, while another group of steps according to various blocks described above may be performed in a continuous mode. For example, an intermittent operational mode may include performance of steps described in blocks450,454,456,458,460, and462(optionally with block452), while a continuous operational mode may include performance of steps described in blocks442,454,456, and462.

In certain embodiments, an ionic liquid/glycerol-based sensing platform allows the a pathogen sensor (e.g., SARS-CoV-2 sensor) to be free of evaporative considerations and provide a durable support for a sensing reaction. A custom-made aptamer whose configuration allows it to only bind to SARS-CoV-2's proteins and provides the selectivity for the robust detection of SARS-CoV-2 on a long-lasting probe. A quantum dot Förster resonance energy transfer (FRET) signal transduction mechanism allows for sensitive readout of viral signal in a single reaction step, free of washing and liquid handling operations. This robust design allows the device to eventually be used in a “plug-and-play” manner without significant procedural (e.g. calibration, reagent replacement) needs to increase reproducibility of SARS-CoV-2 measurements. If the system detects the presence of any SARS-CoV-2 viral antigen, then one or more mitigation modules (which may plug into or otherwise be installed within) a building ventilation control system may take appropriate action (e.g., carrying outdoor fresh air into the room and transporting high aerosol particle air that has been determined to contain pathogen to a disinfection system with a filter and UV light system). This system design conserves outstanding ventilation energy for aerosol mitigation at an estimated daily cost savings (e.g. 23-fold if system activates only once in 24 hours), allowing for data-driven approaches to reduce viral transmission in hospitals and other buildings without significant increases in ventilation energy consumption.

FIGS. 30A-30Care plots of particle concentration versus time illustrating performance of a method (for detecting and mitigating airborne contamination with continuous monitoring and periodic sampling) including steps identified inFIG. 29. InFIG. 30A, particulate levels in an environment are continuously monitored during a first time window465. During a majority of the first time window465, sensed particulate levels are within a baseline range, until an initial spike470is detected. Detection of the initial spike470triggers air sampling, preconcentration, and sensing of one or more target pathogens. As shown inFIG. 30B, the initial spike470may grow to a larger spike470′ while the steps of air sampling, preconcentration, and sensing are performed to identify presence and/or concentration of one or more target pathogens such as SARS-CoV-2. Whether responsive to the initial spike470and/or the positive identification of one or more target pathogens, one or more mitigation modules may be operated to reduce concentration of particles and pathogens in the environment being monitored.FIG. 30Cincorporates the plots ofFIGS. 30A-30B, and shows the effect of operation of one or more mitigation modules. As shown, the first time window465is followed by a second time window466(in which particle concentration initially spikes according to spike470′ but is returned (by declining spike472) to within a baseline level through operation of one or more mitigation modules during a third time window467.

FIG. 31Aillustrates a first collector and sensor assembly480useable with the system ofFIG. 29, including serially arranged first and second impactors484,486, a filter488, and a fan490, wherein the impactors484,486may serve as pathogen sensors. The collector and sensor assembly480includes a first pipe section483arranged to conduct an air sample from an inlet482to the first impactor484, which may be configured to sense larger aerosols (e.g., PM10, from 2.0 μm to 10.0 μm diameter. A second pipe section485is arranged downstream of the first impactor484and is configured to direct the air sample to a second impactor486, which may be configured to sense smaller aerosols (e.g., PM2.5, or 0.2 μm-2.5 μm size range). A third pipe section487is arranged to direct the air sample to a viral/bacterial filter488, which is arranged (together with a fourth pipe section489) between the second impactor486and a fan490that generates subatmospheric pressure to draw an air sample through the collector and sensor assembly480. An air outlet491is arranged downstream of the fan490. The first impactor484and second impactor486have associated first and second impactor wires484A,486A, respectively, coupled to sensors (e.g., CMOS sensor) of the respective impactors484,486, while power signal wires490A are arranged to conduct power from a power source (e.g., battery or AC outlet power, not shown) to the fan490.

FIG. 31Bis a cross-sectional view of a portion of a collector and sensor assembly480′ similar to the assembly480depicted inFIG. 31A, showing serially arranged first and second impactors484′,486′ and a filter488′. The first impactor484′ receives an inlet air sample492from an upstream pipe483′ and conveys it through a first nozzle to a chamber501of the first impactor484′. The inlet air sample492is directed against and around a first preconcentrator/sensor surface500that may include a first CMOS sensor, wherein large aerosols or particles495may be captured by the first preconcentrator/sensor surface500. A continued portion of the air sample494flows downstream to the second impactor486, through a nozzle494to enter a chamber503and be directed against and around a second preconcentrator/sensor surface502that may include a second CMOS sensor, wherein smaller aerosols or particles496may be captured by the second preconcentrator/sensor surface502. A further portion of the air sample then flows through the filter488′ and a downstream pipe489′ due to suction provided by a downstream fan (not shown).

FIG. 32schematically illustrates a sensor assembly510for detecting SARS-CoV-2 virus particles, including a negative control area511, a positive control area531, and a testing area521. The negative control area511includes a substrate512having affixed thereto SARS-CoV-2 N-protein adaptamers labeled with 525 nm quantum dots (collectively, labeled adaptamers514). As shown, MERS N-proteins labeled with 655 nm quantum dots (collectively, labeled competitor antigens) are bound to the labeled adaptamers514in the negative control area511. The positive control area531includes a substrate532having labeled adaptamers534affixed thereto, wherein unlabeled SARS-CoV-2 N-protein antigens538are bound to the labeled adaptamers534. The test area521includes a substrate522having labeled adaptamers524affixed thereto, wherein unlabeled SARS-CoV-2 N-protein antigens528are bound to the labeled adaptamers524, and additional labeled competitor antigens526are present but not bound to the labeled adaptamers524. The test area522may also include labeled adaptamers524′ to which no molecules are bound. In operation, the negative and positive control areas511,531may be insulated from a sample air flow with a transparent chamber and exposed to an impactor detector from the backside of a sensor supporting substrate.

FIG. 33illustrates a SARS-CoV-2 sensor image540obtained with a CMOS detector542, with increasing dilutions of a 525 nm quantum dot labeled protein in ionic liquid. An ionic liquid column543is shown at right, a control (empty) column545is shown at middle left, and numerous sensing areas544show results of increasing dilutions of a 525 nm quantum dot labeled protein in ionic liquid. Green light intensity signals obtained by the CMOS detector542may be analyzed with custom software.

FIG. 34is a calibration curve showing average intensity per exposure in seconds versus quantum dot surface area concentration (picomoles/cm2) in ionic liquid for the sensor image540ofFIG. 33.

FIG. 35schematically illustrates components of a wireless system550for communicating outputs of multiple collector and sensor assemblies (e.g., TRAIS)551A-551C to one or more computer servers (e.g., cloud servers)560using one or more wireless and/or wired communication networks554. Each TRAIS551A-551C may include a pathogen collector and sensor module552and one or more wireless communication elements554(e.g., transceivers). Wireless communications via different mechanisms (e.g., Bluetooth®, ZigBee, WiFi, etc.) may be used. The cloud server allows for access by institutions that can utilize collected data for statistical analyses of SARS-CoV-2 transmission. Right) Representation of airborne SARS-CoV-2 map based on measurements by TRAIS can inform airborne spread of disease through the country. This can help inform public policy regarding prevention of transmission (e.g., business operations, mask wearing, etc.). As shown, measurement data uploaded to the one or more servers560may be used to generate an airborne pathogen (e.g., SARS-CoV-2) map570. Representation of airborne pathogens (e.g., SARS-CoV-2) in a map (with geographic overlay) based on measurements by the pathogen collector and sensor modules551A-551C can inform airborne spread of disease throughout a desired geographic area (e.g., a city, state, nation, or continent). The maps would enable determination of areas of high exposure that can be detrimental to health. Wireless communication can communicate levels of health threats via the cloud for analysis by epidemiologists. The data could be plotted at building level, street level, city level, county, state, regional and country level, thereby providing additional value to government and private organizations. Such data could help inform public policy regarding prevention of transmission (e.g., business operations, mask wearing, etc.).

It is noted that the operational steps described in any of the embodiments herein are described to provide examples and discussion. The operations described may be performed in numerous different sequences other than the illustrated sequences. Furthermore, operations described in a single operational step may actually be performed in a number of different steps. Additionally, one or more operational steps discussed in the embodiments may be combined. Those of skill in the art will also understand that information and signals may be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips, which may be referenced throughout the above description, may be represented by voltages, currents, electromagnetic waves, magnetic fields, particles, optical fields, or any combination thereof.

Those skilled in the art will appreciate that other modifications and variations can be made without departing from the spirit or scope of the invention.

Since modifications, combinations, sub-combinations, and variations of the disclosed embodiments incorporating the spirit and substance of the invention may occur to persons skilled in the art, the invention should be construed to include everything within the scope of the appended claims and their equivalents. The claims as set forth below are incorporated into and constitute part of this detailed description.

It will also be apparent to those skilled in the art that unless otherwise expressly stated, it is in no way intended that any method in this disclosure be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim below does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is no way intended that any particular order be inferred. Moreover, where a method claim below does not explicitly recite a step mentioned in the description above, it should not be assumed that the step is required by the claim.

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