Influence of air pollution and meteorological factors on the spread of COVID-19 in the Bangkok Metropolitan Region and air quality during the outbreak

This study aimed to examine the trend of ambient air pollution (i.e., ozone (O3), nitrogen monoxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), benzene (C6H6) and particulate matter with an aerodynamic diameter smaller than 10 microns (PM10), and non-methane hydrocarbons (NMHCs) at 10 monitoring stations located in the main residential and industrial areas in the State of Kuwait over 6 years (2012–2017). We found that the SO2 level in industrial areas (0.065 ppm) exceeded the allowable range of SO2 in residential areas (0.030 ppm). Air pollution variables were defined by the Environmental Public Authority of Kuwait (K-EPA). In this study, integrated statistical analysis was performed to compare an established air pollution database to Kuwait Ambient Air Quality Guidelines and to determine the association between pollutants and meteorological factors. All pollutants were positively correlated, with the exception of most pollutants and PM10 and O3. Meteorological factors, i.e., the ambient temperature, wind speed and humidity, were also significantly associated with the above pollutants. Spatial distribution mapping indicated that the PM10 level remained high during the southwest monsoon (the hot and dry season), while the CO level was high during the northeast monsoon (the wet season). The NO2 and O3 levels were high during the first intermonsoon season.

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Pollution Characteristics and Policy Actions on Fine Particulate Matter in a Growing Asian Economy: The Case of Bangkok Metropolitan Region

Atmosphere ◽

10.3390/atmos10050227 ◽

2019 ◽

Vol 10 (5) ◽

pp. 227 ◽

Cited By ~ 5

Author(s):

Daiju Narita ◽

Nguyen Oanh ◽

Keiichi Sato ◽

Mingqun Huo ◽

Didin Permadi ◽

...

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Quality Management ◽

Fine Particulate Matter ◽

Policy Implications ◽

Receptor Modeling ◽

Metropolitan Region ◽

Air Quality Management ◽

Fine Particulate

Air pollution is becoming a prominent social problem in fast-growing Asian economies. Taking the Bangkok Metropolitan Region (BMR) as a case, we conducted an observational study of fine particulate matter (PM2.5) and acid deposition, consisting of their continuous monitoring at two sites. To find the major contributing sources of PM2.5, the PM composition data were analyzed by a receptor modeling approach while the pollution load from BMR sources to the air was characterized by an emission inventory. Our data show generally alarming levels of PM2.5 in the region, of which transportation and biomass burning are two major sources. In this paper, we present a general overview of our observational findings, contrast the scientific information with the policy context of air quality management in BMR, and discuss policy implications. In BMR, where a set of conventional regulatory instruments on air quality management are already in place, a solution for the air pollution problem should lie in a combination of air quality regulation and other policies, such as energy and agricultural policies.

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Environmental justice and air quality in Santiago de Chile

Revista de Salud Pública ◽

10.15446/rsap.v17n3.38465 ◽

2015 ◽

Vol 17 (3) ◽

pp. 337-350 ◽

Cited By ~ 3

Author(s):

Richelle Rose Perez

Keyword(s):

Air Pollution ◽

Air Quality ◽

Environmental Justice ◽

Fine Particles ◽

Metropolitan Region ◽

Quality Data ◽

Policy Makers ◽

Pollution Levels ◽

Spatial Differences ◽

Socioeconomic Data

Objective The metropolitan region in Santiago, Chile has an air quality problem. However, the larger issue may lie in the inequities created by the distribution of the air pollution.Methods To assess the inequities created by the spatial differences in air pollution, the author analyzed fine particle pollution levels for 2008-2011 at monitoring stations throughout the region. The author also compared air quality data with socioeconomic data.Results The areas of the Santiago metropolitan region with the worst air quality have lower socioeconomic levels. Pollution in these areas reaches levels higher than the current Chilean 24 hour standard for fine particles. These areas also have longer time periods of unhealthy air and 21 % more days with unhealthy levels of air pollution.Discussion The differences in exposure to pollution create an inequality and environmental injustice among the socioeconomic groups in the metropolitan region. Chilean policymakers have the regulatory tools needed to improve environmental justice. However, they need to improve the implementation of these tools in order to achieve that goal: Chilean policy makers should consider local sources of air pollution in the most polluted municipalities; Government decision makers should make extra efforts to listen to the community and improve access to environmental information; Environmental justice advocates should involve stakeholders from the social justice movement and other related areas; Policy makers should track progress towards environmental justice by evaluating differences in health outcomes related to differential exposure to air pollution in different parts of the Santiago metropolitan area.

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Air Quality Management for Electroplating Industry for Mumbai Metropolitan Region, Maharashtra—Air Quality Management for Electroplating Industry

Energy and Earth Science ◽

10.22158/ees.v3n2p36 ◽

2020 ◽

Vol 3 (2) ◽

pp. p36

Author(s):

Yogesh Gore ◽

Awkash Kumar

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Management ◽

Health Impact ◽

Metropolitan Region ◽

Air Pollution Control ◽

Air Quality Management ◽

Treatment Techniques ◽

Acid Mist ◽

Wet Scrubbers

Electroplating is considered to be a major polluting industry because it discharges toxic materials and heavy metals through effluent like wastewater, air emissions and solid wastes. There are many registered electroplating units in Mumbai Metropolitan Region (MMR). The quantities of gaseous wastes generated from these industries were estimated and the existing control and treatment techniques for these gaseous wastes were evaluated. Further, Air Quality Modeling (AQM) study was also carried out to predict the concentration of acid mist with the help of emission, characteristics of stack and meteorology. A Gaussian plume model based SCREEN View software was used to predict concentrations for two industries which showed that the acid mist emissions from stack were under the consented limits. Further, health impact survey was performed at 1km radius of the industry to study the effects of air pollution on human health. It showed that 47%, 40% and 57% workers near the electroplating industries are suffering from chest pain, eye irritation and breathlessness respectively. Clustering of electroplating industries in the MMR will improve the waste management in the region. Installation of efficient air pollution control equipment like wet scrubbers can eliminate the hazards caused due to acid mist emissions from electroplating industries.

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Application of hysplit model to evaluate the relationship between meteorological parameters and PM10 content in the atmospheric environment in Ha Noi city, Vietnam

Science & Technology Development Journal - Engineering and Technology ◽

10.32508/stdjet.v3i2.715 ◽

2020 ◽

Vol 3 (2) ◽

pp. First

Author(s):

Anh Dung Nguyen ◽

Hồng Sơn Dương ◽

Đức Hạnh Nguyễn Thế ◽

Nguyen Dac Dong

Keyword(s):

Air Pollution ◽

Air Quality ◽

Wind Speed ◽

Wind Direction ◽

Meteorological Parameters ◽

Meteorological Factors ◽

Pollution Sources ◽

Atmospheric Environment ◽

Hysplit Model ◽

The Relationship

Meteorology is one of the factors that plays an important role in assessing the quality of the atmospheric environment. Regarding the air pollution, especially dust and gaseous emissions, there are currently few studies on the relationship between meteorological factors and the increase in pollutant concentration. In this study, the relationship between several meteorological parameters such as temperature (TEM), wind speed (WS), wind direction (WD) and PM10 content in Hanoi were evaluated through the Spearman correlation coefficient (r) by SPSS statistical analysis software. Data includes hourly meteorological factors (temperature, wind speed and wind direction) and 24-h PM10 concentration collected at three automatic air quality monitoring stations in Hanoi in 2018. In addition, HYSPLIT model is used to determine the influence of wind direction and contribution of air pollution sources. The results show a negative correlation (r <0) between PM10 content, temperature and wind speed in dry and rainy seasons. During the dry season, Hanoi has a higher PM10 content than the remaining months of the year. This might be partly affected by outside pollution sources from the North and Northwest. The findings emphasize the dependence of air quality on local meteorological conditions and the distribution of major

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Spatiotemporal Relationships between Air Quality and Multiple Meteorological Parameters in 221 Chinese Cities

Complexity ◽

10.1155/2020/6829142 ◽

2020 ◽

Vol 2020 ◽

pp. 1-25 ◽

Cited By ~ 1

Author(s):

Mengyi Ji ◽

Yuying Jiang ◽

Xiping Han ◽

Luo Liu ◽

Xinliang Xu ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Relative Humidity ◽

Meteorological Factors ◽

Southern China ◽

Great Influence ◽

Pollution Prevention ◽

Spatial And Temporal Variation ◽

Factors Affecting ◽

Temporal Differences

Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM2.5 and PM10. The influence of relative humidity was most significant for SO2 and it also had a great influence on O3, while wind speed had a great influence on NO2. The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives.

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A combined model based on feature selection and support vector machine for PM2.5 prediction

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202812 ◽

2021 ◽

pp. 1-15

Author(s):

Xiaocong Lai ◽

Hua Li ◽

Ying Pan

Keyword(s):

Air Pollution ◽

Support Vector Machine ◽

Feature Selection ◽

Air Quality ◽

Prediction Model ◽

Meteorological Factors ◽

Feature Selection Method ◽

Support Vector ◽

Combined Model ◽

Model Based

With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.

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Relevance Analysis on the Variety Characteristics of PM2.5 Concentrations in Beijing, China

Sustainability ◽

10.3390/su10093228 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3228 ◽

Cited By ~ 6

Author(s):

Binxu Zhai ◽

Jianguo Chen ◽

Wenwen Yin ◽

Zhongliang Huang

Keyword(s):

Air Pollution ◽

Spatial Distribution ◽

Air Quality ◽

Diurnal Variation ◽

Wind Direction ◽

Meteorological Factors ◽

Distribution Characteristics ◽

Wind Speeds ◽

Pollutant Concentrations ◽

Study Results

Air pollution has become one of the most serious environmental problems in the world. Considering Beijing and six surrounding cities as main research areas, this study takes the daily average pollutant concentrations and meteorological factors from 2 December 2013 to 13 October 2017 into account and studies the spatial and temporal distribution characteristics and the relevant relationship of particulate matter smaller than 2.5 μm (PM2.5) concentrations in Beijing. Based on correlation analysis and geo-statistics techniques, the inter-annual, seasonal, and diurnal variation trends and temporal spatial distribution characteristics of PM2.5 concentration in Beijing are studied. The study results demonstrate that the pollutant concentrations in Beijing exhibit obvious seasonal and cyclical fluctuation patterns. Air pollution is more serious in winter and spring and slightly better in summer and autumn, with the spatial distribution of pollutants fluctuating dramatically in different seasons. The pollution in southern Beijing areas is more serious and the air quality in northern areas is better in general. The diurnal variation of air quality shows a typical seasonal difference and the daily variation of PM2.5 concentrations present a “W” type of mode with twin peaks. Besides emission and accumulation of local pollutants, air quality is easily affected by the transport effect from the southwest. The PM2.5 and PM10 concentrations measured from the city of Langfang are taken as the most important factors of surrounding pollution factors to PM2.5 in Beijing. The concentrations of PM10 and carbon monoxide (CO) concentrations in Beijing are the most significant local influencing factors to PM2.5 in Beijing. Extreme wind speeds and maximal wind speeds are considered to be the most significant meteorological factors affecting the transport of pollutants across the region. When the wind direction is weak southwest wind, the probability of air pollution is greater and when the wind direction is north, the air quality is generally better.

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The impact analysis of traffic and meteorological factors on air pollution risk in Taiwan, Taipei City

10.5194/egusphere-egu21-7446 ◽

2021 ◽

Author(s):

Yu-Ting Lin ◽

Yuan-Chien Lin

Keyword(s):

Air Pollution ◽

Air Quality ◽

Linear Regression ◽

Regression Model ◽

Air Pollutants ◽

Stepwise Regression ◽

Meteorological Factors ◽

Multivariate Linear Regression ◽

The Impact ◽

Significant Factors

Air pollution has always been one of the serious issues around the world, not only related to the large-scale climate environment, but also related to local-scale vehicles-caused air pollutants in the city. Generally, diesel-burning vehicles emit NOX, SO2, CO; gasoline burning vehicles emit CO, CO2, NOX respectively. The common air pollutants CO and NOX are widely regarded as the primary traffic-caused air pollutants. Therefore in this study, we take vehicle detector data including car speed, car volume, lane occupy as well as meteorological data and the air pollutants concentration in consider. Firstly, we use the Stepwise Regression Model(SRM) to select the significant factors for the target air pollutants and predict them with multivariate linear regression. Secondly, we also combine Long Short-Terms Memory (LSTM) Model to simulate the highly nonlinear and unstationary complex chemical interaction between air pollutants. In this study we got high model accuracy performance in primary pollutants prediction (CO,NOX) by including the vehicle detector data with both Multivariate linear regression Model and LSTM model which conclude that the vehicle detector data can significantly improve the quality of model prediction. This process select the statistically significant factors of the pollutants, and also establishes a neural network model including traffic, meteorological factors and air quality which contribute to the air pollutants risk management of government agency.Keywords: traffic pollutants, air quality, stepwise regression, LSTM model

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VALORIZATION OF THE URBAN ENVIRONMENT OF SOUTH SERBIA FROM THE AIR QUALITY ASPECT

Knowledge International Journal ◽

10.35120/kij28041329m ◽

2018 ◽

Vol 28 (4) ◽

pp. 1329-1333

Author(s):

Miodrag Šmelcerović

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Environment ◽

Pollution Sources ◽

Anthropogenic Sources ◽

Anthropogenic Factors ◽

Limit Values ◽

Cause And Effect ◽

Sources Of Pollution

The protection of the environment and people’s health from negative influences of the pollution of air as a medium of the environment requires constant observing of the air quality in accordance with international standards, the analysis of emission and imission of polluting matters in the air, and their connection with the sources of pollution. Having in mind the series of laws and delegated legislations which define the field of air pollution, it is necessary to closely observe these long-term processes, discovering cause-and-effect relationships between the activities of anthropogenic sources of emission of polluting matters and the level of air degradation. The relevant evaluation of the air quality of a certain area can be conducted if the level of concentration of polluting matters characteristic for the pollution sources of this area is observed in a longer period of time. The data obtained by the observation of the air pollution are the basis for creation of the recovery program of a certain area. Vranje is a town in South Serbia where there is a bigger number of anthropogenic pollution sources that can significantly diminish the air quality. The cause-and-effect relationship of the anthropogenic sources of pollution is conducted related to the analysis of systematized data which are in the relevant data base of the authorized institution The Institute of Public Health Vranje, for the time period between the year of 2012. and 2017. By the analysis of data of imission concentrations of typical polluting matters, the dominant polluting matters were determined on the territory of the town of Vranje, the ones that are the causers of the biggest air pollution and the risk for people’s health. Analysis of the concentration of soot, sulfur dioxide and nitrogen oxides indicates their presence in the air of Vranje town area in concentrations that do not exceed the permitted limit values annually. The greatest pollution is caused by the soot content in the air, especially in the winter period when the highest number of days with the values above the limit was registered. By perceiving the influence of natural and anthropogenic factors, it is clear that the concentration of polluting matters can be decreased only by establishing control over anthropogenic sources of pollution, and thus it can be contributed to the improvement of the air quality of this urban environment.

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