Factors Affecting Air Pollution Research Articles

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7ppm, 18.94 and 60.76µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Modeling Sulphur Dioxide (SO2) Quality Levels of Jeddah City Using Machine Learning Approaches with Meteorological and Chemical Factors

Modeling air quality in city centers is essential due to environmental and health-related issues. In this study, machine learning (ML) approaches were used to approximate the impact of air pollutants and metrological parameters on SO2 quality levels. The parameters, NO, NO2, O3, PM10, RH, HyC, T, and P are significant factors affecting air pollution in Jeddah city. These factors were considered as the input parameters of the ANNs, MARS, SVR, and Hybrid model to determine the effect of those factors on the SO2 quality level. Hence, ANN was employed to approximate the nonlinear relation between SO2 and input parameters. The MARS approach has successful applications in air pollution predictions as an ML tool, employed in this study. The SVR approach was used as a nonlinear modeling tool to predict the SO2 quality level. Furthermore, the MARS and SVR approaches were integrated to develop a novel hybrid modeling scheme for providing a nonlinear approximation of SO2 concentration. The main innovation of this hybrid approach applied for predicting the SO2 quality levels is to develop an efficient approach and reduce the time-consuming calibration processes. Four comparative statistical considerations, MAE, RMSE, NSE, and d, were applied to measure the accuracy and tendency. The hybrid SVR model outperforms the other models with the lowest RMSE and MAE, and the highest d and NSE in testing and training processes.

Türkiye’de Hava Kirliliği ve Sağlık Harcamaları

Hava kirliliği sebebiyle Türkiye’de her yıl yaklaşık 45 bin önlenebilir erken ölüm gerçekleşmektedir. Hava kirliliği sebebiyle yaşanan rahatsızlıklar ve ölümlerin hem ekonomiye hem de sağlık harcamalarına etkisi olmaktadır. Dolayısıyla bu çalışmada hava kirliliğinden kaynaklı rahatsızlıklar sebebiyle sağlık harcamalarındaki maliyetlerin incelenmesi amaçlanmaktadır. Çalışma verileri OECD İstatistik tarafından sağlanan 2009-2019 yılları arasındaki hava kirliliğine etki eden faktörlerin değişiminden ve HEAL 2021 yılı hava kirliliği kaynaklı sağlık harcamaları maliyetlerinden oluşmaktadır. Araştırmada veriler grafik ve tablo haline getirilerek yorumlanmaktadır. Yapılan incelemeler sonucunda Türkiye’de hava kirliliği sonucu asgari 25,845 milyon TL, azami 52,492 milyon TL sağlık maliyetinin ortaya çıktığı görülmüştür. Hava kirliliği insanları, hayvanları, bitki ve hatta nesneleri etkilemektedir. Bir ülkede ortaya çıkan hava kirliliği atmosferde taşınarak başka bir yerde hava kirliliğine etkide bulunabilir. Hava kirliliği sadece tek bir alanı değil tüm dünyayı etkilemektedir. Bu yüzden küresel işbirliği ile kirletici faktör limitlerinin sıkı önlemlerle gerçekleştirilmesi gerekmektedir. Küresel işbirliği ile yenilenebilir, temiz enerji kaynaklarının kullanımı için teşvik politikalarının oluşturulması daha sağlıklı bir toplum ve güçlü bir ekonominin oluşmasını sağlayacaktır.

Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction

Forecasting air pollution is considered as an essential key for early warning and control management of air pollution, especially in emergency situations, where big amounts of pollutants are quickly released in the air, causing considerable damages. Predicting pollution in such situations is particularly challenging due to the strong dynamic of the phenomenon and the various spatio-temporal factors affecting air pollution dispersion. In addition, providing uncertainty estimates of prediction makes the forecasting model more trustworthy, which helps decision-makers to take appropriate actions with more confidence regarding the pollution crisis. In this study, we propose a multi-point deep learning model based on convolutional long short term memory (ConvLSTM) for highly dynamic air quality forecasting. ConvLSTM architectures combines long short term memory (LSTM) and convolutional neural network (CNN), which allows to mine both temporal and spatial data features. In addition, uncertainty quantification methods were implemented on top of our model's architecture and their performances were further excavated. We conduct extensive experimental evaluations using a real and highly dynamic air pollution data set called Fusion Field Trial 2007 (FFT07). The results demonstrate the superiority of our proposed deep learning model in comparison to state-of-the-art methods including machine and deep learning techniques. Finally, we discuss the results of the uncertainty techniques and we derive insights.

Public Risk Perception and Response to Air Pollution

In recent years, as people pay more and more attention to environmental quality, many environmental problems have gradually become the focus of people’s concern, especially those with greater risks. Air pollution is one of the major risk factors of death worldwide and is also the cause of the increased risk of chronic diseases. Therefore, the purpose of this paper is to understand the correlation between factors affecting air pollution risk perception and the level of risk perception, and whether they can affect coping behaviour. The results showed that among the demographic variables, only marital status and average monthly income were correlated with air pollution risk perception. In addition, the public satisfaction with the air pollution to the air of the negative correlation between perceived risk, and air pollution information knowledge and the positive correlation between perceived risk of air pollution, can confirm air satisfaction and understanding information about air pollution caused by the risk perception of people have a certain influence, and in the case of the higher the risk perception could prompt them to take more coping behaviour, such as wearing a mask, avoid being outdoors for a long time, and so on.

Meteorological Variables and Synoptic Patterns Associated with Air Pollutions in Eastern China during 2013-2018.

Steady meteorological conditions are important external factors affecting air pollution. In order to analyze how adverse meteorological variables affect air pollution, surface synoptic situation patterns and meteorological conditions during heavy pollution episodes are discussed. The results showed that there were 78 RPHPDs (regional PM2.5 pollution days) in Jiangsu, with a decreasing trend year by year. Winter had the most stable meteorological conditions, thus most RPHPDs appeared in winter, followed by autumn and summer, with the least days in spring. RPHPDs were classified into three patterns, respectively, as equalized pressure (EQP), advancing edge of a cold front (ACF) and inverted trough of low pressure (INT) according to the SLP (sea level pressure). RPHPDs under EQP were the most (51%), followed by ACF (37%); INT was the minimum (12%). Using statistical methods and meteorological condition data on RPHPDs from 2013 to 2017 to deduce the thresholds and 2018 as an independent dataset to validate the proposed thresholds, the threshold values of meteorological elements are summarized as follows. The probability of RPHPDs without rain was above 92% with the daily and hourly precipitation of all RPHPDs below 2.1 mm and 0.8 mm. Wind speed, RHs, inversion intensity(ITI), height difference in the temperature inversion(ITK), the lower height of temperature inversion (LHTI) and mixed-layer height (MLH) in terms of 25%–75% high probability range were respectively within 0.5–3.6 m s−1, 55%–92%, 0.7–4.0 °C 100 m −1, 42–576 m, 3–570 m, 200–1200 m. Two conditions should be considered: whether the pattern was EQP, ACF or INT and whether the eight meteorological elements are within the thresholds. If both criteria are met, PM2.5 particles tend to accumulate and air pollution diffusion conditions are poor. Unfavorable meteorological conditions are the necessary, but not sufficient condition for RPHPDs.

What Are the Key Factors Affecting Air Pollution? Research on Jiangsu, China from the Perspective of Spatial Differentiation

Based on the spatial Dubin model, two representative atmospheric pollutants comprising industrial waste gas and NO2 were selected to empirically investigate the effects of eight variables, including the economic growth level, population size, and industrial structure, on atmospheric pollutants and measure the sub-regional effects using data for Jiangsu from 2000 to 2016. The results showed that the population size, economic growth level, rural modernization level, personal income, and introducing high-speed rail were positively correlated with air pollution. By contrast, the industrial structure, foreign direct investment, and urbanization level were negatively correlated with air pollution. In particular, the effects of personal income, foreign direct investment, introducing high-speed rail, and urbanization were significant. The sub-regional analyses detected spatial spillover effects in all regions. The direct effects were greater than the indirect effects, where the level of economic growth, foreign direct investment, and urbanization had significant impacts, and the spatial spillover effects were most obvious in northern Jiangsu and central Jiangsu. Therefore, the following solutions are proposed: adjusting the industrial structure, improving the mode of economic growth, promoting the use of clean energy and rationally introducing foreign direct investment.

Investigating Economic Factors Affecting Air Pollution in Developing and Developed Countries; A Spatial Regime Model Approach

The relationship between the variables e.g. economic growth, energy consumption, foreign direct investment, and the growth of pollution are complex issues, and important for the countries, which are planning a sustainable development strategy. On the other hand, considering the effects of geopolitical spillover on environmental economic issues, this paper studies the effective economic factors on carbon dioxide emissions in the developing and developed countries over the period 1993–2016, by employing the approach of the spatial regime panel. Results show that there are spatial effects and spatial heterogeneity between the variables. Accordingly, in this paper, we employed the regression approach of the spatial regime panel. Results show that the how variables affect the spatial regimes varies, and countries that have the structure of the first or second regime have different effects in terms of size and significance of the coefficients. So that in the study of the effect of spatial autoregression variable in the developing countries in the first spatial structure regime, there are neighboring effects of carbon dioxide emissions; while in the second spatial structure regime, there is no neighboring effects in the dependent variable.

CLASSIFICATION OF HIGH AND LOW LEVEL OF PM10 CONCENTRATIONS IN KLANG AND SHAH ALAM, MALAYSIA

Particulate matter (PM) comprises of a complex mixture of small solid or liquid particles of organic and inorganic elements that floats freely in air. PM10 is defined as a particulate matter with an aerodynamic diameter of 10 m or less. The main objective of this paper is to classify the level of PM10 in selected locations in Peninsular Malaysia using discriminant analysis. Two important components considered in this study, namely; the meteorological factors and pollutant factors. The meteorological factors comprise of wind speed, wind direction, humidity and temperature while pollutant factors consist of Carbon Monoxide (CO), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2) and Ozone (O3). The classification of high or low level of PM10 concentrations was based on the Malaysia Ambient Air Quality Guideline (MAAQG). The findings indicated that the classification equation differs from location to location due to different levels of PM10 concentrations, location of monitoring stations and factors affecting air pollution in that location. The simulation data also verified that the classification of PM10 concentration was almost similar to the real condition that occurred in Klang in October 2015.

Examining the Factors Affecting Air Pollution Emission Growth in China

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995–2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports.

Identifying the main contributors of air pollution in Beijing

Air pollution has become an emerging environmental issue in developing countries like China in the last two decades. Sulphur dioxide (SO2) is one of the major air pollutants that poses significant risks in many areas undergoing a process of industrialization such as Beijing. Realizing the main factor causing environmental quality changes is the key to solving this problem. By using an extended version of IPAT model, this paper aims to identify the main contributors of air pollution in Beijing from 1989 to 2012. The result shows that the most influential factors affecting air pollution in Beijing are affluence and emission intensity. From analyzing the historical background, we conclude that the air pollution change in Beijing is heavily policy-driven.

Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic

Ambient air quality assessment and management plays an important role in the current European Union policy. Among others, air pollution by PM10 is being monitored. A previous analysis of PM10 aimed at identifying factors affecting air pollution in stations in the City of Brno using available observed meteorological variables. However, the studied model cannot be used for predicting the level of PM10 pollution because the included meteorological variables are not available exactly at the time when the prediction is requested. In that case, we should base the predictions on available predicted variables, namely on temperature, wind direction, wind speed, and cloud cover. A comparison of obtained predictions with the observed values of PM10 during a testing period allows us to evaluate a loss of prediction quality when the predicted covariates are used instead of the observed ones. The presented analysis based on test of symmetry and test of homogeneity of the marginal distribution of two–way contingency tables shows that the loss of prediction quality by employing the predicted meteorological variables is non–significant in the studied case. This observation suggests that the model with predicted meteorological variables can be employed in environmental management processes.

Identification of factors affecting air pollution by dust aerosol PM 10 in Brno City, Czech Republic

The statistical analysis of the observation of dust aerosol PM 10 from four monitoring stations of the agglomeration of the city of Brno during a time period from January 1, 1998 until December 30, 2005 is presented. The main meteorological factors affecting air pollution at each station were identified by means of a generalized autoregressive linear model with gamma distribution of the response variable and log-link function. Along with meteorological factors, the influence of the heating season and weekdays on the air pollution was considered. The suggested model can be used for a prediction of the daily mean value of dust aerosol PM 10 at a given station using selected factors and their previous values.