Search

Air pollution is a significant global environmental challenge that can cause health problems affecting everyone on the planet without any geographical boundary. It has a substantial impact directly or indirectly not only on human health but on social and economic activities as well. Researchers worldwide are working to evaluate and predict the air quality index using advanced computational models. These models have drastically altered how we think about and approach API prediction (Air Pollution Index). The primary intent of this paper is to highlight the suitable machine learning models on remotely sensed Sentinel-5P sensor data in assessing and predicting air pollution before, during, and after the lockdown enforced due to the COVID-19 pandemic. This work includes the assessment and prediction of API using four air-polluting parameters- Nitrogen Dioxide, Ozone, Carbon Monoxide, and Sulphur Dioxide in four metropolitan cities of India - Kolkata, Mumbai, Delhi, and Chennai. The paper used Markov Chain as an operator for predicting the AQI state and verified it using ground-level and satellite data. The model's accuracy was estimated using the predicted dataset RMSE (Root Mean Square Error). The outcome of the prediction model was also validated with actual data, which substantiates the finding that during this lockdown period of the COVID-19 pandemic, NO 2 concentration was reduced significantly due to less traffic and energy production from industries. [ABSTRACT FROM AUTHOR]

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual's exposure. Hence, it has potential use for urban planners and the city council's policymakers who are involved in Lancaster Air Quality Management. [ABSTRACT FROM AUTHOR]

Establishing a scientific ecological compensation mechanism for air pollution is crucial for air protection. This study models the ecological compensation mechanism of the Stackelberg differential game between the local regulator and an enterprise with a competitor by introducing the air quality index and the social welfare benefits of the local regulator. Using the Pontryagin maximum principle, this study obtains dynamic strategies for the local regulator and the enterprise while maximizing the benefits. The evolution of the shadow price is analyzed with the inverse differential equation method. Then, the effects of the shadow price on the optimal dynamic strategies are analyzed using numerical simulation, together with the effects of the introduction of social welfare benefits on the efforts of the local regulator to protect the air environment. The conclusions show that introducing social welfare benefits as an ecological compensation criterion for air pollution promotes air protection by the local regulator. [ABSTRACT FROM AUTHOR]

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual’s exposure. Hence, it has potential use for urban planners and the city council’s policymakers who are involved in Lancaster Air Quality Management.

Pollution acquires many forms, such as water, air, noise, heat, and light, whereas air pollution immediately affects human health. Controlling air pollution is a critical task because the sources of air pollution are more and different. Measuring and forecasting the severity level of air pollution helps the public to take safety actions immediately. Hence measuring air pollution precisely and automatically has become the need of the hour. This paper presents a novel Termite Fly Optimization Algorithm (TPO) for predicting the level of air pollution in various places. Based on the pollutants level measured from the air, the TFO algorithm estimates the human health mapping with the termites level in the search space. The high level of pollutants mixed in the air is mapped with the severity level of human health. This paper uses the worst-case comparison between the data and the termites' behavior. The prediction made through this model is converted into an index called the Air quality index (AQI). An open-source dataset is used to train the model, and a real-time dataset of air pollution from Chennai, Manali, is obtained to evaluate its prediction. Air pollution is predicted according to the AQI index. The results are compared with existing models, and the performance is verified.

With the development of urban economy and the improvement of people's living standard, environmental problems have become the focus of people's attention. Among them, air pollution not only restricts the speed of economic development, but also affects the health level of the people. This study used the global spatial autocorrelation method, spatial descriptive statistics, and other index methods to explore the changing trend of spatial and temporal characteristics and influencing factors of urban air pollutants. The results showed that the air pollutions of PM10, SO2, NO2, and air pollution index (API) were mainly concentrated in spring and winter, and there was an obvious "weekend effect." Pollutants had a certain spatial dependence, and the pollution center gradually shrank from the northwest to the northeast of China. Population density and urban economic level had a negative relationship with API and main pollutants, while economic level and API had positive correlation. The results of this study provided references for scientifically coping with the characteristics of seasonal pollution transformation and strengthened the joint prevention and control mechanism of air pollution by exploring the change rules and influencing factors of air pollutants, so as to form a green and coordinated development pattern. [ABSTRACT FROM AUTHOR]

We looked at the long-term and short-term diversified relationships between industrial agglomeration, aggregate energy consumption, residential construction sector growth, and air pollution in China's 30 provincial units from 2004 through 2020. We contributed to the existing knowledge by calculating a holistic air pollution index (API) and applying advanced methods. We also augmented the Kaya identity by including industrial agglomeration and residential construction sector growth in the baseline framework. Based on empirical results: First, we revealed long-term stability among our covariates through panel cointegration analysis. Second, we uncovered a positive bilateral relationship between residential construction sector growth and industrial agglomeration in the long and short term. Third, we unfolded a unilateral positive correlation emerging from aggregate energy consumption to API, displaying the greatest influence in the east zone of China. Fourth, we observed a unilateral positive connection stemming from industrial agglomeration and residential construction sector growth to aggregate energy consumption and API in the long- and short-term dimensions. Finally, the linking nature was homogeneously prevailing across the long term and short term; however, the long-term impact size outweighed that of the short term. Given our empirical results, useful policy insights are discussed to provide the readers with a take-home message for substantiating sustainable development goals. [ABSTRACT FROM AUTHOR]

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora. [ABSTRACT FROM AUTHOR]

Formulation of the problem. Air pollution is one of the biggest modern ecological problem, which aggravates with process of urbanization, industrial development, increasing number of vehicles, etc. So that, the article is devoted to the study the of air pollution in the largest cities of the Volyn region: Lutsk, Kowel, Novovolynsk and Volodymyr-Volynskyi. The purpose of the article is to estimate state and quality of the air in the largest cities of the Volyn region, discover the main sources of air pollution, characterize the effects of pollution on people health and environment, propose measures for solving this problem. Review of previous publications and studies. State of the air pollution in the urban areas of the Volyn region became the subject of scientific interest of many scientists. Hulai L. D., Karaim O. A. and Syniuk, A. Yu. researched ecological state of air basin and structure of sources of air pollution in the Novovolynsk City. Panasiuk M. V. and Hulai L. D. analyzed air quality in the Kovel district and Kovel City. Problems of air pollution were investigated in the studies of Kiptenko Ye. M., Kozlenko T. V. and Molchak Ya. O., Fesiuk V. O., Kartava O. F. Moreover, Poruchynska, I. V. in her publication explored ways of reducing air pollution by vehicle in the Volyn region. Methods. The most common methods in the research were analysis and synthesis, correlations and regressions, generalization and comparison. Moreover, to study the quality of air basin in the largest urban area – Lutsk City, the authors calculated index of air pollution. Results. The study defined that the main source of air pollution in the urban systems are automobiles, which produce into the atmosphere 85-95 % of all emissions. Furthermore, stationary sources such as industrial enterprises and utility companies also pollute air basin of cities with hazardous substances. However, the amount of pollutants from these sources in the Lutsk City and Volodymyr-Volynskyi City slowly decreases. The authors researched that the air basin in Lutsk City is exposed to the greatest anthropogenic loading. According to the calculation of the air pollution index, city’s air basin belongs to the quality class «polluted air». Consequences of air pollution are quite different and have impact on environment and economic complex. Besides, poor air quality can cause different diseases. To solve this problem the authors propose to reconstruct and modernize enterprises’ treatment systems, to increase the number of environmental friendly public transport, to increase the areas with greenery, to introduce green logistics in transportation. The authors concluded that air basin in the most urbanized areas of the Volyn region is quite polluted, which caused by using vehicles and functioning of manufacture. So that, it is suggested measures to reduce the amount of emission from different source of pollution. Scientific novelty and practical significance. The main preconditions and trends of modern changes in air quality of the largest cities in the Volyn region was researched for the first time. Furthermore, the authors identified the main sources of air pollution, which have affected the quality of urban air basins during recent years. In addition, index of air pollution in the Lutsk City was calculated for the period 2015-2020. The article proposes measures to improve air quality and reduce emissions from stationary and mobile sources of pollution.

The work is devoted to the study of the impact of coal thermal power plants on the environment. A brief analysis of scientific works dedicated to the study of the impact of thermal power plants on the environment was conducted. The analysis of publications shows that the negative impact of thermal power plants on the environment is quite complex, as it includes both atmospheric air pollution by gas and aerosol emissions, as well as thermal energy emissions into the environment and soil pollution. The volume of emissions of polluting substances into the air of Cherkassy region by electricity supply enterprises and the dynamics of the comprehensive air pollution index were analyzed, which showed that in recent years there has been a trend of rapid growth of this indicator. With the help of the cartographic zoning model of the city territory and the indicator of the total environmental load, which is an indicator of aerotechnogenic pollution of landscapes, performed using the SURFER software package, it was determined that the city undergoes a significant aerogenic load, the spread of aerotechnogenic pollution from the southern industrial agglomeration reaches a significant distance and within the radius of action covers the entire territory of the city. A study of population health risk assessment based on annual averages for chronic exposure to atmospheric pollution showed that the population risk is 12.4 times higher than the limit value. The overall non-carcinogenic risk is 7.75 (there is a risk of developing adverse effects in most of the population). The respiratory organs (HQ=7.42) and the immune system (HQ=2.67) are most affected. [ABSTRACT FROM AUTHOR]

Long-term trends in sunshine duration in Chinese cities have been closely linked to factors caused by air pollution. To understand this impact on sunshine duration (SD), surface solar radiation from 1981 to 2020, annual PM2.5 concentration from 2012 to 2020 and air pollution index (API) data from 2013 to 2020 collected in ten representative cities in China were investigated, and the long-term relationship of SD with diffuse fraction (DF), aerosol option depth (AOD), annual PM2.5 concentration and API were analyzed. The results indicated that trends in SD varied across cities. SD decreased in seven of the ten selected cities' stations in the past 40 years, and the annual mean SD decreased from −0.03 h d−1 per decade to −0.36 h d−1 per decade—particularly in the Beijing North China Plain, Shanghai and Wuhan stations in the Yangtze River delta, where the trend coefficients were lower than −0.5. Conversely, increases in varying degrees of SD were found in Kunming (0.38 h d−1 per decade), Guangzhou and Shenyang in Southwest, South and Northeast China, respectively—with the biggest trend coefficient of 0.54 in Kunming. In addition to the SD variation, the DF in the ten city stations increased continuously from 1981 to 2010 and then declined after 2010, which is closely related to decreases in the annual PM2.5 concentration after 2012. The correlation coefficients between DF and SD ranged from −0.04 to −0.62, validating their negative relationship and the slight increasing trend in SD in recent ten years. The annual averages for SD and the DF plateaued in the 2010s due to the stringent pollution controls established by the Chinese government after 2010. Furthermore, the correlation coefficients between SD and the API ranged from −0.12 to −0.58, demonstrating a negative relationship between SD and the API. [ABSTRACT FROM AUTHOR]

Introduction. Air pollution is an important factor influencing the quality of life and health of peoples. It is increasing due to urbanization, development of industrial and transport complexes of cities. The same tendencies are characteristic for the state of air pollution in Lutsk. In recent years, the level of its pollution is estimated as high. And in the first half of 2018, Lutsk was among the top three cities with the highest level of air pollution in Ukraine. Although the town is not a major industrial center. The purpose of article. The purpose of the study is assessment and analyze the current level of air pollution in Lutsk for many years (2011-2019). Methods. Research methods – expeditionary (for research the ecological condition of the urban area), methods of mathematical statistics (for statistical processing of monitoring results), expert assessment (to establish the causes of pollution and environmental risks). The materials of own researches of an ecological condition of the Lutsk territory, and also results of laboratory researches of atmospheric air pollution on stationary posts in the town are used. They were conducted by the Integrated Laboratory for Environmental Pollution Observations of the Volyn Regional Center for Hydrometeorology. Results. The structure of pollutant emissions in the town is analyzed. It is established that the share of emissions from mobile sources (vehicles) increased from 61.4% in 1990 to 92.2% in 2015 and 94.6% in 2020. For the period from 2009 to the present the air pollution index (API) for the Lutsk town varies between 7.7 and 10.49 (high level of pollution). The size of the API slightly decreased until 2015, and then increased until 2018. The place of Lutsk in the ranking of the most polluted cities in Ukraine correlates with the value of the API. In 2013-14 API was relatively low – 7.99-8.55. Lutsk occupied 22 steps. Since 2016, API for the Lutsk has started to increase. The city rating rose from 9 to 7 steps. Concentrations of nitrogen dioxide, phenol and formaldehyde in the air of the town constantly exceed the maximum concentration limits. The scientific novelty is to analyze the dynamics of the content in the town air of substances whose concentrations exceed the maximum allowable, to study the causes of pollution, to develop a set of measures to reduce air pollution in Lutsk. Practical significance is determined by the possibility of using the obtained results to develop a set of measures to reduce air pollution in Lutsk.

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora.

Air pollution is associated with several detrimental health conditions. This study assessed comfort parameters, priority air pollutants, hydrogen sulphide (H2S), non-methane hydrocarbons (NMHCs), and volatile organic compounds (VOCs) in natural science departments in a university to understand their role in air pollutant concentrations in university environments and associated health risks. Levels of air pollutants in the departments varied. High CO2 concentrations existed in all departments with highest levels of NMHC and VOC observed in the biochemistry, microbiology and biotechnology (BMBT) department. Highest Air quality index value of 111.3 was recorded for NO2 in the BMBT department. Health risk associated with exposure to these pollutants was highest for occupants in the physiology, followed by the biodiversity, and finally BMBT department. Natural science departments seem to contribute significant amounts of H2S, NO2, NMHCs and TVOCs in university campuses. Additional ventilation and frequent monitoring of air quality in these departments are recommended. [ABSTRACT FROM AUTHOR]

Mining is a significant part of the transforming economy, which is generally considered as essential as well as social evil at the same time. It is one of the potential contributors to air and water pollution and possesses long-term impact on their quality. Keeping in view the exponential mining activities, we have selected an iron mine area in Bailadila, Chhattisgarh, India, as a sampling site and investigated the impact of mining activities on the air as well as water quality by setting up seven air quality and thirty water quality monitoring stations. From the results obtained, it was observed that concentration of air pollutants such as SO2, NO2, PM2.5 and PM10 for the year 2015 lies in the range of 11.5–13.0 µg/m3, 11.5–13.0 µg/m3, 24.9–33.4 ppm and 61.6–74.2 ppm, respectively, while for the year 2018, it lies in the range of 10.3–11.7 µg/m3, 10.5–14.7 µg/m3, 18.3–50.8 ppm and 23.7–60.7 ppm, respectively. Furthermore, results obtained revealed that air pollutants such as SO2, NO2, PM2.5 and PM10 were within the permissible limits but they contributed towards the light air pollution (air pollution index: 25–50) at all the air monitoring stations. Moreover, PM10 was considered as criterion pollutant in the Bailadila, Chhattisgarh region. On the other hand, it was observed that groundwater quality was deteriorated in the subsequent years. Most of the water quality parameters were in the permissible limits except iron (Fe). Moreover, on the basis of water quality indexing, water quality was classified as "poor" in ~ 30% of the sites and "very poor" in ~ 34% sites. The water quality was "unhealthy for drinking" in 3% and 6% sites in the year 2015 and 2018, respectively. [ABSTRACT FROM AUTHOR]

The gaseous particulate pollutants in the atmosphere are closely related to human health, especially the respiratory system. In order to explore the impact of short-term exposure to atmospheric particulates on the number of people's respiratory disease visits, we collected daily atmospheric pollutant data and meteorological data as well as data of the number of people's respiratory disease visits from a hospital from October 2018 to November 2020 in Tianjin. For respiratory system disease consultation data, the Poisson regression model of time series data is used to analyze the relationship between the daily average concentration of atmospheric particulate matter and the daily visits for respiratory system diseases. After analyzing the influence of meteorological factors, long-term trends, seasonal trends, day-of-week effects, holiday effects and other factors, the study shows that for every 10p.g/m3 increase in atmospheric PM 10 concentration, the relative risk (RR) of the emergency number of respiratory diseases in the population is 1.0027 (95%CI: 1.0008-1.0046), and the RR value of female is slightly higher than that of men, which are 1.0028 (95%CI: 1.0004-1.0051) for female and 1.0026 (95%CI: 1.0004-1.0049) for male, respectively. Finally, it is believed that the short-term increase in atmospheric PM10 concentration in Tianjin may lead to an increase in the number of emergency visits for respiratory diseases. People who exercise outdoors or engage in outdoor activities should strengthen self-protection to prevent air pollution from bringing health effects to their own respiratory system. [ABSTRACT FROM AUTHOR]

Microalgae are an effective source of bioindicators to screen any changes of the natural ecosystem in the environment. With the increase in air pollution due to different potential sources, pollution monitoring stations are deployed across the nation with sophisticated analytical methodologies. However, phytoplankton can also serve as an indicator of air pollution as they possess the ability to absorb particulate contaminants. With previous studies on indicator species based on aquatic habitats, this study was carried out to comprehend the interaction between gaseous oxides of sulphur and nitrogen, particulate matter with diameter less than 10 and 2.5 μm (PM10, PM 2.5), with the population density, abundance and frequency of terrestrial epiphytic microalgae from residential and commercial areas of Chennai District, Tamil Nadu. There was a significant correlation between the members of Chlorophyceae, Bacillariophyceae and Cyanophyceae with the abovementioned air pollutants. Cluster analysis, CCA and NMDS analysis were done to find the interaction pattern. The results suggested few indicator species from genera Chlorella, Phormidium, Nitzschia, Navicula, Spirogyra and Oscillatoria. Species of Desmodesmus, Gleocapsa, Gleocyctis and Lyngbya can be considered as an addition to Palmer's list of tolerant species. During this study, the occurrence of filamentous Cladophora crispata is reported for the first time from Tamil Nadu, India. This study unveiled the list of microalgal species resistant to air pollutants from one of the highly polluted cities of India. [ABSTRACT FROM AUTHOR]

Formulation of the problem. Air pollution is one of the biggest modern ecological problem, which aggravates with process of urbanization, industrial development, increasing number of vehicles, etc. So that, the article is devoted to the study the of air pollution in the largest cities of the Volyn region: Lutsk, Kowel, Novovolynsk and Volodymyr-Volynskyi. The purpose of the article is to estimate state and quality of the air in the largest cities of the Volyn region, discover the main sources of air pollution, characterize the effects of pollution on people health and environment, propose measures for solving this problem. Review of previous publications and studies. State of the air pollution in the urban areas of the Volyn region became the subject of scientific interest of many scientists. Hulai L. D., Karaim O. A. and Syniuk, A. Yu. researched ecological state of air basin and structure of sources of air pollution in the Novovolynsk City. Panasiuk M. V. and Hulai L. D. analyzed air quality in the Kovel district and Kovel City. Problems of air pollution were investigated in the studies of Kiptenko Ye. M., Kozlenko T. V. and Molchak Ya. O., Fesiuk V. O., Kartava O. F. Moreover, Poruchynska, I. V. in her publication explored ways of reducing air pollution by vehicle in the Volyn region. Methods. The most common methods in the research were analysis and synthesis, correlations and regressions, generalization and comparison. Moreover, to study the quality of air basin in the largest urban area – Lutsk City, the authors calculated index of air pollution. Results. The study defined that the main source of air pollution in the urban systems are automobiles, which produce into the atmosphere 85-95 % of all emissions. Furthermore, stationary sources such as industrial enterprises and utility companies also pollute air basin of cities with hazardous substances. However, the amount of pollutants from these sources in the Lutsk City and Volodymyr- Volynskyi City slowly decreases. The authors researched that the air basin in Lutsk City is exposed to the greatest anthropogenic loading. According to the calculation of the air pollution index, city’s air basin belongs to the quality class «polluted air». Consequences of air pollution are quite different and have impact on environment and economic complex. Besides, poor air quality can cause different diseases. To solve this problem the authors propose to reconstruct and modernize enterprises’ treatment systems, to increase the number of environmental friendly public transport, to increase the areas with greenery, to introduce green logistics in transportation. The authors concluded that air basin in the most urbanized areas of the Volyn region is quite polluted, which caused by using vehicles and functioning of manufacture. So that, it is suggested measures to reduce the amount of emission from different source of pollution. Scientific novelty and practical significance. The main preconditions and trends of modern changes in air quality of the largest cities in the Volyn region was researched for the first time. Furthermore, the authors identified the main sources of air pollution, which have affected the quality of urban air basins during recent years. In addition, index of air pollution in the Lutsk City was calculated for the period 2015-2020. The article proposes measures to improve air quality and reduce emissions from stationary and mobile sources of pollution. [ABSTRACT FROM AUTHOR]

This paper examines the spatial relationship between the ambient air pollution level of an apartment and its property value in the housing market of South Korea. Using detailed transaction data for 2015–2018, we construct the air pollution index and estimate a two-stage spatial Durbin error model that controls for both direct and spillover effects. We find that, holding other factors equal, a 1% increase in the air pollution level can, on average, cause a decrease in the value of a local real property by 0.32% ($879). Spatially heterogeneous effects of air pollution on housing prices are investigated, and air pollution is found to have a more significant direct impact on the urban housing market than in rural areas. Moreover, rising air pollution levels in urban centers can raise housing prices in suburban and rural areas, suggesting a strong spillover effect of air pollution and potential migration towards better air quality. The findings in this paper have profound implications for analyzing the spatial impacts of air pollution on housing prices and urban development. [ABSTRACT FROM AUTHOR]

Accurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution. [ABSTRACT FROM AUTHOR]

Removing environmental pollution has become one of the main concerns of mankind today. Air quality in cities depends on the weather and the amount of pollutants produced. Today, air pollution is one of the most complex problems in human societies, which has had many negative effects on the health of living things, especially humans. In this study, the average monthly and annual concentrations of air pollutants in the air pollution measuring stations of Tehran Environmental Protection Bureau were evaluated in 91 to 97 years.The results showed that the highest concentration of major pollutants in Tehran in 1997 had a relatively decreasing trend compared to the previous year. Comparative statistics of the mean annual concentration of indicator pollutants show that CO levels in all stations decreased in 97 at most stations and O3 at all stations compared to the previous year, (it should be explained that the ozone pollutant at Shofuf station in 1997 to the geophysical station has been transferred). The average concentration of NO2, SO2, PM10 and PM2.5 pollutants in air pollution measuring stations and comparing it with the (WHO) standard indicates that all stations are in poor condition and above the standard level.

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

The holy city of Haridwar is now emerging as a key industrial destination of the north India due to the presence of IIE Haridwar. Due to increased anthropogenic activities like vehicular emissions and industrial discharges in the Haridwar region, air pollution levels has raised greatly. The objectives of the study were (1) to determine the Spatial and seasonal variations of air quality in and around IIE, Haridwar and (2) to find out the Air Pollution Index (API). For this monthly monitoring of pollutant gases (SO2 and NO2) and particulate matter (SPM and RSPM) for two years (2012 and 2013) were done at six ambient air quality monitoring locations, one at each land use area (rural/agricultural, urban residential, protected area, industrial and commercial land use). The average SPM, RSPM, SO2, NO2 in the study area were found as 86.92 µg/m3, 81.04 µg/m3, 23.23 µg/m3, 24.73 µg/m3, respectively. It was observed that the average SPM levels exceed WHO standard limit of 120 µg/m3 at IIE Haridwar during study period. The average API during study period was 50.14 which indicates light air pollution. Meteorological conditions along with the distance from sources, particularly road transportation play a major role in deciding the ambient air quality over a region. [ABSTRACT FROM AUTHOR]

Air pollution is a problem faced by most countries across the globe. The modeling and evaluation of the probabilistic behavior of air pollution are crucial in providing useful information that can help in managing the environmental risk and planning for the adverse effects of air pollution. For modeling of air pollution, several statistical approaches have been considered; however, only a few approaches have been used for addressing the uncertainty in the analysis. This study proposes a new application of the Markov chain-based fuzzy states (MCFS) model using triangular fuzzy numbers for analysing the uncertainty in the occurrence of air pollution events and describing the transition behaviour of air pollution. In this study, the air pollution index (API) data collected from the city of Klang in Malaysia for a period between 2012 and 2014 is considered in the analysis. Based on the API data, a five-state Markov chain is considered for representing the five fuzzy states of air pollution. The fuzzy transition probabilities are estimated and used to determine the characteristics of air pollution such as the steady state probabilities and the mean first passage time for each state of air pollution. The findings show that, in general, the risk of occurrences for unhealthy events in Klang is small, nonetheless remains notably troubling. The results demonstrate that the MCFS can effectively model the air pollution index and it could be a better option in predicting air pollution. It may provide valuable information and more understanding about the dynamics of air pollution to the experts and policymakers. This will enable them to develop proper strategies to manage air quality. [ABSTRACT FROM AUTHOR]

Long-term trends in sunshine duration in Chinese cities have been closely linked to factors caused by air pollution. To understand this impact on sunshine duration (SD), surface solar radiation from 1981 to 2020, annual PM2.5 concentration from 2012 to 2020 and air pollution index (API) data from 2013 to 2020 collected in ten representative cities in China were investigated, and the long-term relationship of SD with diffuse fraction (DF), aerosol option depth (AOD), annual PM2.5 concentration and API were analyzed. The results indicated that trends in SD varied across cities. SD decreased in seven of the ten selected cities’ stations in the past 40 years, and the annual mean SD decreased from −0.03 h d−1 per decade to −0.36 h d−1 per decade—particularly in the Beijing North China Plain, Shanghai and Wuhan stations in the Yangtze River delta, where the trend coefficients were lower than −0.5. Conversely, increases in varying degrees of SD were found in Kunming (0.38 h d−1 per decade), Guangzhou and Shenyang in Southwest, South and Northeast China, respectively—with the biggest trend coefficient of 0.54 in Kunming. In addition to the SD variation, the DF in the ten city stations increased continuously from 1981 to 2010 and then declined after 2010, which is closely related to decreases in the annual PM2.5 concentration after 2012. The correlation coefficients between DF and SD ranged from −0.04 to −0.62, validating their negative relationship and the slight increasing trend in SD in recent ten years. The annual averages for SD and the DF plateaued in the 2010s due to the stringent pollution controls established by the Chinese government after 2010. Furthermore, the correlation coefficients between SD and the API ranged from −0.12 to −0.58, demonstrating a negative relationship between SD and the API.

The study of various air pollutants and meteorological parameters are very important for all the researchers. Baleswar was known to be a seaside districts of odisha which is the economic and cultural heart of Northern Odisha. The aim of this study is to measure the air pollutants, meteorological parameters and to enumerate the air pollution index at three specific sites (Sahadevkhunta, Mallikashpur, Rasalpur) according to CPCB procedures. The air pollutants analysed by supplying through specific absorbing reagents and the pollutants were analysed up to 3 year (2017, 2018 and 2019) with a regularity of thrice per week. Analyses of our data sets showing that SO2 and NO2 concentration during summer, rainy and winter season are within the prescribe standard of NAAQS by CPCB but PM10 and PM2.5 are above the prescribed standard except PM2.5 concentration of rainy season in year 2019. Air pollution index is remaining in the condition between clean air (CA) to moderate air pollution (MAP) and it shows that the pollution index in all the sites are reducing from the year 2017 to 2019 may be due to enhancing technologies to reduce the pollutant concentration in air. [ABSTRACT FROM AUTHOR]

Introduction. Air pollution is an important factor influencing the quality of life and health of peoples. It is increasing due to urbanization, development of industrial and transport complexes of cities. The same tendencies are characteristic for the state of air pollution in Lutsk. In recent years, the level of its pollution is estimated as high. And in the first half of 2018, Lutsk was among the top three cities with the highest level of air pollution in Ukraine. Although the town is not a major industrial center. The purpose of article. The purpose of the study is assessment and analyze the current level of air pollution in Lutsk for many years (2011-2019). Methods. Research methods - expeditionary (for research the ecological condition of the urban area), methods of mathematical statistics (for statistical processing of monitoring results), expert assessment (to establish the causes of pollution and environmental risks). The materials of own researches of an ecological condition of the Lutsk territory, and also results of laboratory researches of atmospheric air pollution on stationary posts in the town are used. They were conducted by the Integrated Laboratory for Environmental Pollution Observations of the Volyn Regional Center for Hydrometeorology. Results. The structure of pollutant emissions in the town is analyzed. It is established that the share of emissions from mobile sources (vehicles) increased from 61.4% in 1990 to 92.2% in 2015 and 94.6% in 2020. For the period from 2009 to the present the air pollution index (API) for the Lutsk town varies between 7.7 and 10.49 (high level of pollution). The size of the API slightly decreased until 2015, and then increased until 2018. The place of Lutsk in the ranking of the most polluted cities in Ukraine correlates with the value of the API. In 2013-14 API was relatively low - 7.99-8.55. Lutsk occupied 22 steps. Since 2016, API for the Lutsk has started to increase. The city rating rose from 9 to 7 steps. Concentrations of nitrogen dioxide, phenol and formaldehyde in the air of the town constantly exceed the maximum concentration limits. The scientific novelty is to analyze the dynamics of the content in the town air of substances whose concentrations exceed the maximum allowable, to study the causes of pollution, to develop a set of measures to reduce air pollution in Lutsk. Practical significance is determined by the possibility of using the obtained results to develop a set of measures to reduce air pollution in Lutsk. [ABSTRACT FROM AUTHOR]

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices’ influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its’ real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects.

Forecasting air pollution in big cities is of great importance, and there are various types of air pollution indices (APIs). In the present study, the level of air pollution in winter 2011 at four point-locations in Belgrade, Serbia, was measured using wind speed data and non-standard and standard air temperature data, measured at 10 and 2 m, respectively. Using multiple linear regression (MLR) analysis, equations to forecast the API were obtained. This forecast was verified using data from the winter of 2012/2013. The results obtained are well aligned with the monitored API and verified by the root mean square error (RMSE). It is shown that standard meteorological measurements representative of the city can accurately predict the API at individual point-locations as well as using temperature and wind speed measured at each respective location. Three locations, which measured SO2, NO2, PM10, O3 and CO, showed poor air quality for > 78% of days observed. For the fourth location, the final estimate of prevailing air quality could not be calculated due to the absence of PM10 measurements. Forecasting the API on a short-term scale can be of great help for long-term air-quality improvements. [ABSTRACT FROM AUTHOR]

Periodic and continuous monitoring of the air pollutants becomes a very important activity for all researchers worldwide. In Sambalpur, one of the major towns in the Western flanks of Odisha State in Eastern India, a work was envisaged to measure the gaseous and particulate pollutants and compute the air pollution index (API) at four representative sampling stations (Budharaja, Modipara, Sakhipara and Kacheri) based on the guidelines of Central Pollution Control Board (CPCB), New Delhi. The gaseous pollutants were analyzed by passing them through their respective absorbing reagents, while the particulate pollutants were studied gravimetrically for a period of 1 year (August 2015–July 2016) with a frequency of twice per week. The results revealed that the gaseous pollutants (NO2 from 14.64 to 19.19 and SO2 from 3.22 to 5.29 µg/m3) are well within the National Permissible Standards, but the particulate matters (PM10 from 67.34 to 95.08 and PM2.5 from 36.38 to 64.00 µg/m3) are above the annual permissible limit set by the CPCB. Therefore, the API fluctuated between 37.94 and 57.30. Meteorology and wind rose had some role to play in the pollutant dispersal and dilution, besides being aided by the topography and anthropogenic activities of the area. The air quality of the area swung from light air pollution to moderate air pollution condition, which is primarily attributed to the particulate matters (PM10 and PM2.5). Therefore, continuous, periodic monitoring with attention to source reduction in these pollutants and green belt development are the need of the hour. [ABSTRACT FROM AUTHOR]

Air pollution control in the United States for five common pollutants--particulate matter, ground-level ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide--is based partly on the attainment of ambient air quality standards that represent a level of air pollution regarded as safe. Regulatory and health agencies often focus on whether standards for short periods are attained; the number of days that standards are exceeded is used to track progress. Efforts to explain air pollution to the public often incorporate an air quality index that represents daily concentrations of pollutants. While effects of short-term exposures have been emphasized, research shows that long-term exposures to lower concentrations of air pollutants can also result in adverse health effects. We developed an aggregate index that represents long-term exposure to these pollutants, using 1995 monitoring data for metropolitan areas obtained from the U.S. Environmental Protection Agency's Aerometric Information Retrieval System. We compared the ranking of metropolitan areas under the proposed aggregate index with the ranking of areas by the number of days that short-term standards were exceeded. The geographic areas with the highest burden of long-term exposures are not, in all cases, the same as those with the most days that exceeded a short-term standard. We believe that an aggregate index of long-term air pollution offers an informative addition to the principal approaches currently used to describe air pollution exposures; further work on an aggregate index representing long-term exposure to air pollutants is warranted.

Solar energy is the primary resource for all biological, chemical and physical processes. The amount of global solar radiation is an important parameter for solar energy applications. It is common to estimate a monthly average of daily global solar radiation using different regression models. These models in turn exploit the correlation between solar radiation and various atmospheric factors. These factors are commonly derived from meteorological, geographical and climatological data that are readily available for majority of weather stations across the world. In this paper, a novel regression model that can predict location-independent daily global solar radiation is presented. The proposed exponential quadratic model captures the correlation between measured global solar radiation values, sunshine hour and Air Pollution Index for Indian cities. In addition to this, an extended study of several other regression models (e.g. linear, quadratic, exp.-linear and exp.-quadratic) is also presented. This analysis with real data from Indian cities suggests that air pollution is a more significant factor than location when predicting solar radiation. Finally, the model parameters (regression coefficients) for each model are listed out. Additionally, the generalised model equation for the best performing model is also presented.

It is shown that the chronic atmospheric pollution of cities in the Baikal region is caused by the combination of natural and climatic features and the industrial development of the territory. The variations in emissions of pollutants in 2008–2017 indicate an impact of the periods of growth and slowdown of macroeconomic processes in the regions and the transformation of the main production. A dominant role of thermal power engineering and processing industry as well as of road transport in the urban atmospheric pollution in the region is established. A significant increase in average annual concentrations of benzo[a]pyrene is revealed. A stable relationship was found between this growth and the population morbidity (neoplasms, respiratory diseases). The directions for reducing environmental hazards in the Baikal region cities are outlined. [ABSTRACT FROM AUTHOR]

Atmospheric environmental quality significantly influences the environment of people's life and the development of modern society. Therefore, in this paper, we aim to accurately assess atmospheric environmental quality. Firstly, we propose and analyze two indicator systems for atmospheric environmental quality assessment, that is, 1) the standard air pollution index, and 2) an index system which covers different types of influencing factors related to air quality. In particular, the proposed index system is made up of seven parts, that is, 1) Climate and meteorology, 2) Economic and social development, 3) Industrial structure, 4) Energy consumption, 5) Industrial pollutant discharge, 6) The greening of the city, and 7) Environmental protection construction. Secondly, we propose a hybrid AHP-Fuzzy Comprehensive Evaluation Model to evaluate the atmospheric environmental quality. In the hybrid model, AHP is used to calculate the weight coefficient of the index system, and fuzzy comprehensive evaluation model is exploited to describe the vagueness of remark in the evaluation process. Finally, experimental results demonstrate that using the proposed index system, the hybrid AHP-Fuzzy comprehensive evaluation model is able to assess air quality more accurately than the modified API. [ABSTRACT FROM AUTHOR]

For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn. (2) The monthly variation in air pollution in Shanghai was not significant, whereas the seasonal variation of air pollution in Lanzhou was obvious. (3) Air pollution in Shanghai showed an ascending tendency, whereas that in Lanzhou presented a descending tendency. Overall, air pollution in Lanzhou was higher than that in Shanghai, but the situation has reversed since 2015. (4) The primary cycles of air pollution in these two cities were close, but the secondary cycles were significantly different. The aforementioned differences were mainly due to the impact of topographical and meteorological factors in Lanzhou, the weather process and the surrounding environment in Shanghai. These conclusions have reference significance for Shanghai and Lanzhou to control air pollution. The multi-timescale variation and local features of the wavelet analysis method used in this study can be applied to varied aspects of air pollution analysis. The identification of cycle characteristics and the monitoring, forecasting, and controlling of air pollution can yield valuable reference. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the social economy, the problem of urban air pollution has become increasingly serious. This has become a global issue that directly threatens the sustainable development of the national economy and the health of human health. Strengthening the prediction and research of air pollution is of great significance. Air pollution is an important transmission mechanism that affects the inequality of human exercise health indicators. Because of the different state of human exercise and the ability to avoid the risk of air pollution index, the air pollution index will lead to different exposure levels and human exercise health indicators. A game model was explored to explore the relationship between air pollution index and human exercise health indicators. [ABSTRACT FROM AUTHOR]

The study evaluated air quality around host communities of oil companies in Ogba/Egbema/Ndoni Local Government Area in Rivers state. Detailed literature reviews and appropriate multi-gas observing instruments were utilized to obtain air quality information. Results of SO2 in Omoku, Obrikum, Ebocha land Control (5km) from Oil Company indicated 214.1μg/m3, 0μg/m3, 71.4μg/m3 and 28.5μg/m3. NO2was 106μg/m3, 0.0μg/m3, 51,3μg/m3 and 0.0μg/m3 respectively. CH4 was 98,120μg/m3, 35,680μg/m3 and 44600μg/m3 and 28.7μg/m3 respectively. PM2.5 was 54μg/m3, 50.5μg/m3, 64μg/m3 and 50μg/m3 respectively. CO was 93.8μg/m3, 62.8μg/m3, 78.1μg/m3 and 0.0μg/m3 respectively while PM10 was 36μg/m3, 32μg/m3, 35.5μg/m3 and 24μg/m3 respectively. Results indicated that host communities were exposed to reasonable concentrations of air pollutants, especially hydrocarbon, carbon monoxide, nitrogen dioxide, and PM2.5which fall within moderate to severe air pollution level. The statistical analysis of results using ANOVA showed that the difference among the pollutants characteristics at p<0.05 are statistically significant. Federal and state governments should pay particular attention to air pollution problems in the study area by establishing air quality monitoring stations through relevant agencies. [ABSTRACT FROM AUTHOR]

Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels’ U statistic. Also, the model’s validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality.

In this paper, we study the multifractal characteristics and cross-correlation behaviour of Air Pollution Index (API) time series data through multifractal detrended cross-correlation analysis method. We analyse the daily API records of nine air pollutants of the university of Hyderabad campus for a period of three years (2013–2016). The cross-correlation behaviour has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, it is found that the cross-correlation analysis shows anti-correlation behaviour for all possible 36 bivariate time series. We also observe the existence of multifractal nature in all the bivariate time series in which many of them show strong multifractal behaviour. In particular, the hazardous particulate matter PM 2.5 and inhalable particulate matter PM 10 shows anti-correlated behaviour with all air pollutants. [ABSTRACT FROM AUTHOR]

Understanding how urban form is related to air pollution is important to urban planning and sustainability, but the urban form-air pollution relationship is currently muddled by inconsistent findings. In this study, we investigated how the compositional and configurational attributes of urban form were related to different air pollution measures (PM2.5, API, and exceedance) in 83 Chinese cities, with explicit consideration of city size and seasonality. Ten landscape metrics were selected to quantify urban form attributes, and Spearman’s correlation was used to quantify the urban form-air pollution relationship. Our results show that the urban form and air pollution relationship was dominated by city size and moderated by seasonality. Specifically, urban air pollution levels increased consistently and substantially from small to medium, large, and megacities. The urban form-air pollution relationship depended greatly on seasonality and monsoons. That is, the relationship was more pronounced in spring and summer than fall and winter, as well as in cities affected by monsoons. Urban air pollution was correlated more strongly with landscape composition metrics than landscape configuration metrics which seemed to affect only PM2.5 concentrations. Our study suggests that, to understand how air pollution levels are related to urban form, city size and seasonality must be explicitly considered (or controlled). Also, in order to mitigate urban air pollution problems, regional urban planning is needed to curb the spatial extent of built-up areas, reduce the degree of urban fragmentation, and increase urban compactness and contiguity, especially for large and megacities. [ABSTRACT FROM AUTHOR]

Based on the air pollution observation data for 25 cities in the middle reaches of the Yangtze River from January 2015 to November 2016, the feature classification of urban air pollution is studied by a fuzzy clustering analysis. The quantitative analysis of the classification results shows that the results that are based on different pollutant components are significantly different, and the differences in the urban air pollution characteristic categories under the same pollutant component classification criteria are obvious. For example, the classification difference for the nine cities in Jiangxi Province between the classification results according to the characteristics of PM2.5 and truncated set level λ = 0.885 and the characteristics of CO and the truncated set level λ = 0.90 is 0.7143. The 25 major cities in the middle reaches of the Yangtze River are classified according to the CO characteristics and the truncated set level λ = 0.87. They are classified into 5 categories, namely, {Wuhan, Yichang, Shiyan, Xiangyang, Jingzhou, Huanggang, Changsha, Yueyang, Xiangtan, Zhuzhou, Henyang, Nanchang, Jiujiang, Ganzhou, Jingdezhen, Yingtan, Jian, Yichun, Xinyu}, {Xiaogan, Xianning, Huangshi}, {Yiyang}, {Loudi} and {Shangrao}, and their category center distances and their proportions to the category extreme differences are (0.28, 37%), (0.28, 37%), (0.28, 37%), (0.28, 37%), (0.28, 37%) and (0.28, 37%), respectively. Finally, some suggestions on the classification and treatment of urban air pollution are recommended. [ABSTRACT FROM AUTHOR]

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills. [ABSTRACT FROM AUTHOR]

Air pollution index (API) is used in Mainland China and includes only SO2, NO2 and PM10. In 2016, air quality index (AQI) replaced API. AQI contains three more air pollutants (CO, O3 and PM2.5). Both the indices emphasize on the effect of a single pollutant, whereas the contributions of all other pollutants are ignored. Therefore, in the present work, a novel air quality index (NAQI), which emphasizes on all air pollutants, has been introduced for the first time. The results showed that there were 19 d (5.2%) in API, 28 d (7.7%) in AQI and 183 d (50.1%) in NAQI when the indices were more than 100. In API, PM10 and SO2 were regarded as the primary pollutants, whereas all five air pollutants in AQI were regarded as primary. Furthermore, four air pollutants (other than the CO) in NAQI were regarded as primary pollutants. PM10, as being the primary pollutant, contributed greatly in these air quality indices, and accounted for 51.2% (API), 37.0% (AQI) and 52.6% (NAQI). The results also showed that particulate matter pollution was significantly high in Luzhou, where stricter pollution control measures should be implemented. [ABSTRACT FROM AUTHOR]

China's tremendous economic growth during the past three decades has resulted in worsening air quality in most of its cities. However, the spatiotemporal patterns and underlying drivers of air pollution in China remain poorly understood. To address this issue, we used stepwise regression to identify major socioeconomic, climatic, and urban form factors influencing air pollution in 69 major cities across China. Our results showed that social factors such as population size and density were positively correlated with emissions of PM2.5, PM10, NOx, and SO2. Economic factors such as Gross Domestic Product (GDP) and GDP of secondary industry were positively correlated with industry and transportation emissions but negatively correlated with residential emissions of air pollutants. Urban form attributes such as measures of urban fragmentation and contiguity were important in explaining patterns of emissions from residential, power generation, and transportation sectors. As for climatic factors, higher precipitation, higher wind speed, and higher temperatures were all negatively correlated with air pollution levels. We also found that the effects of socioeconomic, climatic, and urban from factors on air pollution levels varied considerably among seasons and between the annual and seasonal scales. Our findings have useful implications for urban planning and management for controlling air pollution in China and beyond. [ABSTRACT FROM AUTHOR]

Recently, there is increasing public awareness of the real time air quality due to air pollution can cause severe effects to human health and environments. The Air Pollutant Index (API) in Malaysia is measured by Department of Environment (DOE) using stationary and expensive monitoring station called Continuous Air Quality Monitoring stations (CAQMs) that are only placed in areas that have high population densities and high industrial activities. Moreover, Malaysia did not include particulate matter with the size of less than 2.5µm (PM2.5) in the API measurement system. In this paper, we present a cost effective and portable air quality measurement system using Arduino Uno microcontroller and four low cost sensors. This device allows people to measure API in any place they want. It is capable to measure the concentration of carbon monoxide (CO), ground level ozone (O3) and particulate matters (PM10 & PM2.5) in the air and convert the readings to API value. This system has been tested by comparing the API measured from this device to the current API measured by DOE at several locations. Based on the results from the experiment, this air quality measurement system is proved to be reliable and efficient. [ABSTRACT FROM AUTHOR]