## 3 Overview and Theoretical Foundations
### Meteorological and Pollution Data Analytics  
Analyzing meteorological and pollution data is necessary for determining ecosystem evolution and analyzing patterns that may influence the future health of city dwellers. It tracks meteorological and pollution changes in a geographical context across time. Meteorological and pollution variables captured daily by meteorological agencies include weather parameters and values such as atmospheric pressure, temperature, humidity, and precipitation. The weather parameters are the same as the weather parameters, and the weather parameters are the same as the weather parameters. Figure 1: Geohash encoding for the city of Rome, Italy, precision 6 for (**a**), and precision 5 for (**b**). Figure 2: A schematic diagram of the filter-and-refine join algorithm. ity, precipitation, solar radiation, and wind speed. Meteorological data comprise massive sources of information readily available for deep insightful analytics. In addition to the meteorological variables, a pyramid of polluting agent's concentrations is captured. With adverse effects of climate change and the awareness of the impact of our daily dynamics influences environment, the reduction of pollution has been a great concern for governmental bodies and people around the globe. Air pollution includes gaseous substances that are harmful to the climate and humans alike. Among those, carbon monoxide (CO), sulfur dioxide (SO\({}_{2}\)) and nitrogen dioxide (NO\({}_{2}\)). Particulate Matter (PM) remains one of the most harmful forms of air pollution agents. Those are artificially and naturally created particles of thin matter suspended in the air. Those PMs have been designated as Group 1 carcinogen, as there is a direct correlation between an increase in PM concentrations and percentage rise in lung cancer incidents [40]. The numbers associated to PMs refers to the diameter of the particles in micrometers (\(\upmu\)m); PM\({}_{10}\) particles are coarse-grained, where PM\({}_{2.5}\) are fine-grained. The concentration of PM\({}_{10}\) in the area around Bologna was examined as pollution data for the scope of the project. The area, together with the rest of Northern Italy and other EU-28 urban cities have been experiencing a rise of particulate matter concentrations over the years, thus raising concerns about their adverse effect on population community health. In recent years, the lockdown attributable to COVID-19 pandemic has caused a reduction in the dust concentrations, as vehicle mobility has been restricted [41; 42; 43]. Meteorological and pollutions data are collected normally on two forms, Network Common Data Form (NetCDF) and GRIdded Binary or General Regularly-distributed Information in Binary (GRIB). NetCDF is an array-based representation for storing multidimensional data. A multidimensional array, having various variables with many dimensions for every variable. An example includes temperature, precipitation, or wind speed across time (a.k.a. space time-series data). It is typical in scientific data (e.g., oceanic, and atmospheric) for storing spatial time series data, for example storing meteorology and remote sensing data. GRIB on the other hand is typical in meteorology for representing weather data (historical and forecast), which is defined by the World Meteorological Organization (WMO). It is typically multidimensional files storing meteorological data in the form of sequential byte array. Figure 3 shows an example of such data. Integrating Mobility and Meteorological Data for Joint Analytics  
PM\({}_{10}\) in addition to other harmful atmosphere concentrations of substances can be integrated with human and vehicle mobility data to highlight trends and decide counteractive measures for protecting community health in urban areas.