A spatial feature engineering algorithm for creating air pollution health datasets

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Air pollution-induced issues involve public health, environmental, agricultural and socio-economic aspects. Therefore, decision-makers need low-cost, efficient tools with high spatiotemporal representation for monitoring air pollutants around urban areas and sensitive regions. Air pollution forecasting models with different time steps and forecast lengths are used as an alternative and support to traditional air quality monitoring stations (AQMS). In recent decades, given their eligibility to reconcile the relationship between parameters of complex systems, artificial neural networks have acquired the utmost importance in the field of air pollution forecasting. In this study, different machine learning regression methods are used to establish a mathematical relationship between air pollutants and meteorological factors from four AQMS (A-D) located between Çerkezköy and Süleymanpaşa, Tekirdağ. The model input variables included air pollutants and meteorological parameters. All developed models were used with the intent to provide instantaneous prediction of the air pollutant parameter NOx within the AQMS and across different stations. In the GMDH (group method of data handling)-type neural network method (namely the self-organizing deep learning approach), a five hidden layer structure consisting of a maximum of five neurons was preferred and, choice of layers and neurons were made in a way to minimize the error. In all models developed, the data were divided into a training (%80) and a testing set (%20). Based on R2, RMSE, and MAE values of all developed models, GMDH provided superior results regarding the NOx prediction within AQMS (reaching 0.94, 10.95, and 6.65, respectively for station A) and between different AQMS. The GMDH model yielded NOx prediction of station B by using station A input variables (without using NOx data as model input) with R2, RMSE and MAE values 0.80, 10.88, 7.31 respectively. The GMDH model is found suitable for being employed to fill in the gaps of air pollution records within and across-AQMS.

Lack of Correlation Between Air Pollution and Acute-Onset Atrial Fibrillation

Estimating multi-annual PM2.5 air pollution levels using sVOC soil tests: Ashkelon South, Israel as a case study

Evaluation of SO 2 pollution levels between four different types of air quality monitoring stations

Air pollution has become a serious concern across the world in the last few decades. In specific cities, the air quality index value had changed from very unhealthy to a hazardous level of health concern. Air pollution has a serious impact on daily lives in those cities. Monitoring of air pollution is becoming necessary these days. Air quality monitoring stations are installed to get the air pollution data, which indicates in the air quality index (AQI) value. In order to contain a proper air quality index (AQI) value, it is essential to locate the air quality monitoring stations in the appropriate place of the study area. Several techniques were being used for site selection of air quality monitoring stations for the last few decades. In this short review, all such techniques have been studied systematically, and comprehensive analysis has been reported for further use by the scientific community and policymakers. In this study, the methods used in the site selection of air quality monitoring stations were categorized into four groups. (1) Multi-Criteria Decision Making (MCDM) techniques; (2) Geographical Information System (GIS); (3) hybrid techniques; and (4) miscellaneous. In the site selection of air quality monitoring stations, the decision-makers should consider various parameters based on the study area. While considering various parameters, the problem solving becomes complex. At this point, MCDM techniques, GIS, and Hybrid techniques are found to be helpful tools for the decision-makers.

The Mediterranean Basin is recognized as a Biodiversity Hotspot for conservation priorities, mainly due to the high biodiversity of plants. Different environmental protection categories of the territory can help to ensure the long-term preservation of biodiversity and habitats. Among them, National Park is the legal category providing the highest protection for nature areas with remarkable ecological and cultural value. These areas represent valuable locations for long-term monitoring of ecological conservation and ecosystem functioning. The Spanish National Parks Network comprises 16 natural areas, including mountain ranges, volcanic habitats, different types of forest, scrublands and grasslands, and aquatic and marine ecosystems. All the National Parks are part of the European Natura 2000 network. Moreover, 6 of them are part of the LTER-Spain network. By law, National Parks must include a long-term Monitoring and Evaluation Plan for the Network, including ecological, sociological, and functional monitoring programmes. Within the ecological monitoring programme, besides flora, fauna and ecosystem functioning assessments, climate is also considered as one of the main drivers of global change. Tree crown condition is assessed in all the parks with forests following the methodology of ICP-Forest Level I. The next step is to include air quality, since atmospheric pollution is another important driver of global change that can affect ecosystem health and functioning. Particularly tropospheric ozone and atmospheric nitrogen deposition are expected to be exceeding the limit values for vegetation protection. Air quality monitoring networks are mainly focused on the protection of human health, thus some very valuable areas for biodiversity might not be represented by air monitoring stations. In addition, atmospheric deposition is not included at the air quality monitoring stations. Moreover, the extension of some of the National Parks, the complex orography and the different climate, morphology and air pollution sources that influence the National Parks, represent a challenge for managers to design an air quality monitoring program with an efficient use of resources. To address this challenge and provide the information needed to design an air quality network in National Parks, a new project is being developed combining local monitoring with air quality modelling. The spatial representativeness of background rural monitoring stations has been assessed through relating CHIMERE chemistry-transport (v2013) model simulations at 5 x 5 km2 resolution and measured data at the stations in Spain (including the Canary Islands) for the years 2019-2023 considering NO2, SO2, O3, PM10 and atmospheric deposition. With this analysis, the suitability of current monitoring stations to represent the air quality in National Parks is being assessed. A risk analysis of the effects on terrestrial ecosystems using CLRTAP critical levels and loads is being performed using the CHIMERE model in the different National Parks. Additionally, a new air quality and deposition monitoring programme has been initiated in 2025 in all the National Parks to measure NO2, SO2, O3 and NH3 using passive samplers and atmospheric wet deposition using bulk collectors connected to ion-exchange resins. Local monitoring data will be used to determine the spatial variability of air quality and deposition within the National Parks and will be compared with modelled data. Effects on aquatic ecosystems are being assessed applying the methodology of CLRTAP ICP-Waters in all the National Parks with suitable locations, including the Canary Islands. Preliminary results will be presented discussing representativeness of current monitoring stations, monitoring techniques available in remote locations and the modelling analyses required.

The article discusses the technologies for creating a network of public monitoring of the state of atmospheric air pollution in the city, which involves independent monitoring based on resolutions and other guidelines on air quality. It is proposed to introduce public environmental monitoring of atmospheric air quality on the basis of indicative measurement of air quality indicators through sensor sensors. The possibilities of using the Oxygen air quality monitoring station, which allows real-time monitoring and recording of the air condition, namely temperature, humidity, PM2.5 and PM10 dust concentration, CO (carbon monoxide), NO2 (nitrogen oxides) and NH3 (ammonia), are analyzed. It has been shown that the functionality of the Oxygen automatic air quality monitoring station allows not only to obtain operational data on air quality, but also to accumulate big data for assessing and predicting pollution indices and risks to public health using a personal account on the EcoCity website [1]. During the experimental studies, the air quality monitoring station measured the maximum concentrations of CO, NH3, PM10 and PM2.5 in the air that did not exceed the MPCs, while periodically exceeding the concentration of nitrogen dioxide NO2. The data obtained indicate a harmful impact, mainly of vehicles, on the environment and public health in the area. In further studies, it is planned to assess the risks to public health in the Pechersk district of Kyiv in accordance with the guidelines "Assessment of the risk to public health from air pollution" [2], taking into account the indicators of other public monitoring stations located in the study area.

Evaluating air quality monitoring stations in Turkey by using multi criteria decision making

Air pollution has become one of the most critical global challenges, exacerbated by climate change and the growing human population. It poses a significant threat to public health, particularly in urban areas where high population density and increased vehicle numbers contribute to poor air quality. The primary motivation of this study is to estimate street-scale air pollution in Beşiktaş, İstanbul, Türkiye, with the Operational Street Pollution Model (OSPM®). Beşiktaş district is a hotspot for traffic density with approximately 170,000 residents, and it hosts critical roadways connecting the European and Asian sides of the city. Understanding air pollution at this scale in Beşiktaş is crucial due to the area’s high population density and traffic volume, significantly impacting public health and urban air quality. The road connecting the Beşiktaş district to the İstanbul Bosphorus was selected as a Case Study and our Area of ​​interest (AOI). This study provides an overview of the data collected, including air quality measurements (PM10, PM2.5, NO2) from a nearby Air Quality Monitoring Station (AQMS), meteorological data from Turkish State Meteorological Service (TSMS), and geographic and traffic data from İstanbul Metropolitan Municipality. A representative 800-meter-long road segment was selected for modeling, focusing on traffic-related air pollution using hourly vehicle data. In this study, air pollution measurement data such as PM10, PM2.5, and NOX are evaluated on the street in the Beşiktaş region with AirGIS and OSPM® modeling to be subsequently analyzed. Air quality data (PM10, PM2.5, NO2) were obtained from a nearby AQMS, while meteorological data were obtained from the TSMS, 3.5 km from the street. Geographic data and traffic data were obtained from İstanbul Metropolitan Municipality.  According to the World Health Organization (WHO), PM2.5 and NO2 concentration values ​​exceeded the limit value every day, while PM10 exceeded it for 13 days during the study period. The highest traffic density occurred at 10:00 a.m, and the average number of vehicles was found to be 1,942. In terms of traffic emissions, gasoline vehicles in total 31,829, which has a much larger share compared to diesel vehicles (7,234). The temporal changes (hourly and daily) in air pollution will be analyzed by this model. At the same time, a correlation analysis will be made between the model's concentration values and those measured by AQMS. This model enables both short- and long-term assessments of air pollution exposure, contributing to studies on human health impact.

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China

Air pollutants may cause severe respiratory problems in the children during their developmental stages. In this study, the respiratory health of school children belonging to three different localities namely industrial, commercial and residential areas of Delhi City was assessed by questionnaire survey and lung function test. The outdoor air pollution data from the National Ambient Air Quality Monitoring (NAAQM) stations was used for the study, which were located within 1-km distance from the selected school sites. Indoor air quality monitoring has also been done at classrooms for particulate matter (PM10), sulphur dioxide (SO2) and nitrogen oxides (NOx). Indoor and outdoor PM10 concentration at schools in commercial area (815 ± 354.45 and 337 ± 85 μg/m3) was ten times above the NAAQM limits of 100 μg/m3. PM10 concentration was lower in industrial (694.6 ± 322.9 and 274 ± 78 μg/m3) and residential (534.3 ± 94.22 and 197 ± 48 μg/m3) areas. However, levels of SO2 and NO2 were under the permissible limits. The findings also revealed that the children living in the commercial area had higher respiratory symptoms as compared to industrial and residential areas. The respiratory symptoms were found to have a statistically significant positive association with PM10 levels in the air.

In recent days the emission of air pollutants in to the environment has grown very quickly. The air pollution is caused due to the smoke emitted by vehicles, industries and other sources. Hyderabad is the largest and capital city of Telangana. It occupies 650km2 on the Deccan Plateau along the banks of the Musiriver, in the northern part of south India. According to the 2011 Census of India, Hyderabad is the fourth-most populous city in India with a population of 6.9 million residents within the city limits, and has a population of 9.7 million residents in the metropolitan region, making it the sixth-most populous metropolitan area in India. With an output of US$74 billion, Hyderabad has the fifth-largest urban economy in India. In India air pollution is monitored by Central Pollution Control Board(CPCB) along with State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with seven sensor stations 248 towns and cities have the air quality network of 591 air quality monitoring stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing the number to 883 to achieve the goal of 1500 by 2024. For our study because of some practical difficulties and analysis of the air quality. Air Pollutants has been collected from the CPCB. The dataset contains City, Date, Time, PM2.5,PM10, NO2, SO2, CO, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into six zones, air quality monitoring sensors are located at 14 places in and around six zones. For our study we selected one sensor that is Secunderabad, which covers the surrounding areas including Jawahar Nagar treatment plant. To analysis the maximum concentration of air pollutants such as PM2.5,PM10,SO2,NO2,CO,O3over the time period of three months (Jan to Mar 2023).Most physio-chemical properties, including as pH, EC, Alkalinity, Chlorides, Cu, Mn, Pb, and Cr, exceeded their acceptable limits at more than three sample sites, according to this study. The water sources were determined to be unsafe for drinking, agricultural, and irrigation uses due to elevated toxicity levels.It was observed that PM2.5,ranges 218 - 403µg/m3 is very poor causes respiratory illness, PM10 range 112 - 360µg/m3 is moderately polluted causes breathing discomfort ( Asthma, lungs), NO2 range 10 – 12ppm is satisfied causes minor breathing discomfort to sensitive people ,O3 range 20 – 35ppm is good causes minor effect, SO2 is 12 – 22ppm is satisfied causes minor breathing discomfort to sensitive people ,CO 109 – 119ppm is severe effects on healthy people and serious impact for those with existing diseases, so continuous air quality monitoring is necessary to protect environment and human health

Air Pollution Index (API) is used in Malaysia to determine the daily air quality status, which is calculated based on the daily concentrations of particulate matter (PM10), ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen dioxide (NO2). This study presents short-term air quality trends based on API from the 52 air quality monitoring stations nationwide between 2010 and 2016. The air quality data and meteorological conditions were obtained from the Department of Environment and used for the API calculation. The API value is classified into six categories, namely: Good (0-50), Moderate (51-100), Unhealthy (101-200), Very Unhealthy (201-300), Hazardous (301-500), and Emergency (above 500). The coefficient of variation (CV) and Mann-Kendall trend test (MK) were used to assess the API variation and trend in each air quality monitoring station. Between the study periods, the API values were largely varied. Observation at 32 air quality monitoring stations have shown significant but small increasing trends, while 12 stations showed significant decreasing trends, and the remaining 8 stations showed no significant trends. The frequency of exceedance (API>50) was used to assess the percentages of unhealthy days. The analysis has found that air quality in Klang Valley was experiencing the highest number of unhealthy days, while the two Malaysian states in Borneo (Sabah and Sarawak) to be relatively less polluted.