Air Pollution Management Research Articles

Land Use and the Climatic Determinants of Population Exposure to PM2.5 in Central Bangladesh

The major industrial cities of Bangladesh are experiencing significant air-pollution-related problems due to the increased trend of particulate matter (PM2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM2.5 and land use and climatic variables to identify the riskiest areas and population groups using a geographic information system and regression analysis. The results show that about 41% of PM2.5 concentration (μg/m3) increased within 19 years (2002–2021) in the study area, while the highest concentration of PM2.5 was found from 2012 to 2021. The concentrations of PM2.5 were higher over barren lands, forests, croplands, and urban areas. From 2002–2021, the concentration increased by about 64%, 62.7%, 57%, and 55% (μg/m3) annually over barren lands, forests, cropland, and urban regions. The highest concentration level of PM2.5 (84 μg/m3) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (hPa) (r2 = −0.26), evaporation (kg m−2) (r2 = −0.01), humidity (kg m−2) (r2 = −0.22), rainfall (mm/h) (r2 = −0.20), and water vapor (kg m−2) (r2 = −0.03) were negatively correlated with PM2.5. On the other hand, air temperature (k) (r2 = 0.24), ground heat (W m−2) (r2 = 0.60), and wind speed (m s−1) (r2 = 0.34) were positively correlated with PM2.5. More than 60 Upazilas were included in the most polluted areas, with a total population of 11,260,162 in the high-risk/hotspot zone (1,948,029 aged 0–5, 485,407 aged 50–69). Governmental departments along with policymakers, stainable development practitioners, academicians, and others may use the main results of the paper for integrated air pollution mitigation and management in Bangladesh as well as in other geographical settings worldwide.

Detection of PM2.5 spatiotemporal patterns and driving factors in urban agglomerations in China

As important urban gathering places for new urbanization construction, urban agglomerations (UAs) have long suffered from high concentrations of PM2.5 pollution. By analyzing the changes of the temporal and spatial distribution patterns and the natural and human drivers of PM2.5 in UAs in China can provide an effective reference basis for atmospheric pollution prevention and control. In this study, based on PM2.5 concentration data from 2000 to 2020, PM2.5 pollution in 19 UAs in China was investigated. First, the annual average PM2.5 concentration was used to analyze the temporal variation, spatial distribution, and agglomeration characteristics of PM2.5 pollution in each UA. Then, factor detection was used to analyze the driving mechanisms of the effects of natural and human factors on PM2.5 pollution in UAs in China. Finally, the explanatory intensities of the interactions between different factors of PM2.5 pollution were analyzed based on interaction detection. The results show that from 2000 to 2020, the PM2.5 concentration exhibited an evolutionary trend of “M”-shape. An inflection point occurred in 2007, and 2020 had the lowest value for the study period. PM2.5 of high concentration took the Central Plains and the Beijing-Tianjin-Hebei UAs as the core of pollution. 2013 was an important turning point with a spatial pattern of expansion to the surrounding areas and then contraction to the center. UAs presented an elliptical spatial distribution, and outliers occurred in individual years. Humidity and temperature were the dominant natural factors for the variation of the PM2.5 concentration in 2013, 2018, and 2020, and their q-values were 0.276(humidity), 0.429(temperature), and 0.375(temperature), respectively. The total population and energy consumption were the dominant human factors for these three years, with q-values of 0.150(total population), 0.259(total population), and 0.196(energy consumption), respectively. The explanatory effect of the two-factor interaction was higher than that of a singular factor. Furthermore, the factor and interaction detection of natural factors found them to be more significant driving mechanisms than human factors. The results of this study not only allow for the understanding of the spatiotemporal variation of the PM2.5 concentration in UAs and the effects of different influencing factors, but also provide a scientific reference for the steady development of UAs and air pollution prevention and management in China.

Port supply chain integration and sustainability: a resource-based view

PurposeIt is claimed that port supply chain integration (PSCI), thanks to its attributes, holds a potential to trigger a port to strategically sustain performance- and competitiveness-measures through strengthening and/or reinforcing an array of port sustainability aspects. This paper aims to empirically investigate the existent influence of PSCI on economic and environmental pillars of port sustainability.Design/methodology/approachBased on a resource-based view, this paper hypothesises that PSCI has a certain impact on port “economic” sustainability (PECS) and port “environmental” sustainability (PENS). Measures of PSCI, PECS and PENS are refined and validated using data collected from the maritime logistics industry in Egypt, and structural equation modelling is employed to test the hypothetical relationships.FindingsThe results indicate that a port having adopted an integrative strategy into supply chains could enhance its cost and operational efficiency, financial and investment situation, while offering high-quality services to its customers. Similarly, PSCI is emphatically correlated with water and air pollution management, energy efficiency and green port management practices.Research limitations/implicationsHaving identified the acute potential of PSCI for sustainable development in maritime logistics and supply chains, this line of research allows port operators and/or authorities to better understand strategic options with which they are able to improve their sustainability practices. This paper is, however, limited only to two dimensions of sustainability by not exploring the “social” aspect of port sustainability due to data-related issues.Originality/valueThis line of research could be regarded as an extended application from other industrial sectors to the port industry in a way to empirically examine the inclusive relationship of PSCI with economic and environmental parameters. The findings from this research make a due contribution to the field of port sustainability in general and Egyptian ports in particular.

The dynamic relationship among economic development, air pollution, and health production in China: the DNSBM efficiency model

China’s economy has grown rapidly in response to the adoption of a high-energy, high-emissions development model, which has led to varying degrees of air pollution; moreover, the corresponding health damage has become a major concern for the public. Studies of the interrelationships between the economy, air pollution, and health often use empirical methods such as regression analysis, to explore the impacts of economic growth and air pollution, human health or air pollution on human health in isolation, and they rarely explore the interactions between the economy, air pollution, and health in terms of efficiency (i.e., the maximum output per unit of input resources that can be produced). Thus, this study constructed a Dynamic Network SBM efficiency model that unifies the production of economic development, air pollution, and health into a single framework. The article reports the findings of a comprehensive study of economic development, air pollution, and health management data for 30 Chinese provinces from 2015 to 2020. The results show that: 1) the overall efficiency (0.693) of China’s two stages from economic production input to healthy output from 2015 to 2020 is low, and there is much room for improvement; 2) the efficiency of economic development (0.729) is higher than the efficiency of health production (0.657), indicating that the health production stage needs to be strengthened; 3) during the study period, China’s PM2.5, SO2, and NOx control efficiency was low, at 0.786, 0.710, and 0.718, respectively, indicating that more effective measures are needed to improve environmental efficiency; and 4) there are significant differences in economic, environmental, and health efficiency across regions. China’s developed eastern provinces are more efficient in terms of economic development, health production, and air pollution control. In contrast, the central and western provinces, which are relatively backward in their economic development, are less efficient. This means that each province should implement sound policies based on its own assessment to promote sustainable economic development while enhancing air pollution mitigation and health promotion.

Impact assessment of air pollutants and greenhouse gases on urban heat wave events in the Beijing-Tianjin-Hebei region.

The production and quality of human life have been impacted by the extreme heat wave events caused by global warming and urbanization. This study analyzed the prevention of air pollution and the strategies of emission reduction based on decision trees (DT), random forests (RF), and extreme random trees (ERT). Additionally, we quantitatively investigated the contribution rate of atmospheric particulate pollutants and greenhouse gases to urban heat wave occurrences by combining numerical models and big data mining technology. This study focuses on changes in the urban environment and climate. The main findings of this study are as follows.The average concentrations of PM2.5 in the northeast of Beijing-Tianjin-Hebei in 2020 were 7.4%, 0.9%, and 9.6% lower than those in the corresponding years of 2017, 2018, and 2019, respectively. The carbon emissions in the Beijing-Tianjin-Hebei region showed an increasing trend during the previous 4years, which was consistent with the spatial distribution of PM2.5. In 2020, there were fewer urban heat waves, which was attributable to a reduction of 75.7% in emissions and an improvement of 24.3% in the prevention and management of air pollution. These results suggest that the government and environmental protection agencies need to pay attention to changes in the urban environment and climate to diminish the negative effects of heatwaves on the health and economic growth of the urban population.

A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network

Precise and efficient air quality prediction plays a vital role in safeguarding public health and informing policy-making. Fine particulate matter, specifically PM2.5 and PM10, serves as a crucial indicator for assessing and managing air pollution levels. In this paper, a daily pollution concentration prediction model combining successive variational mode decomposition (SVMD) and a bidirectional long short-term memory (BiLSTM) neural network is proposed. Firstly, SVMD is used as an unsupervised feature-learning method to divide data into intrinsic mode functions (IMFs) and to extract frequency features and improve short-term trend prediction. Secondly, the BiLSTM network is introduced for supervised learning to capture small changes in the air pollutant sequence and perform prediction of the decomposed sequence. Furthermore, the Bayesian optimization (BO) algorithm is employed to identify the optimal key parameters of the BiLSTM model. Lastly, the predicted values are reconstructed to generate the final prediction results for the daily PM2.5 and PM10 datasets. The prediction performance of the proposed model is validated using the daily PM2.5 and PM10 datasets collected from the China Environmental Monitoring Center in Tianshui, Gansu, and Wuhan, Hubei. The results show that SVMD can smooth the original series more effectively than other decomposition methods, and that the BO-BiLSTM method is better than other LSTM-based models, thereby proving that the proposed model has excellent feasibility and accuracy.

Mitigating Air Pollution in Indian Cities with the Use of Bio Facades

Abstract: Today, the biggest issues facing the planet are air pollution and, as a result, global warming. These problems may affect the climate, human health. To mitigate air pollution frequently concentrate on reducing the sources of the pollutants. This approach can significantly lower the amount of new air pollutants, but it has little impact on the air's already-existing contaminants. By recognizing the current situation, this study seeks to limit the level of harm to humans by regulating air pollution through municipal engineering, facade structures, and architectural design. The air quality of the urban environment can be significantly influenced by the façades of buildings. The ability of nature to manage air pollution has been used to the realm of architecture. Therefore, this study compares three different forms of bio-façades (namely, Water façade, Green façade, and Microalgae) by utilizing nature's capacity to manage carbon dioxide. These facades do not harm human health and do not require any special technology. . Then, after identifying the aforementioned biological façade kinds, we compared their strength and weaknesses, cost, environmental stability and chose the best one. Which façade system is best according to Delhi and luck now’s air pollution condition. Purpose: The goal of this study is to gain a better understanding of how can we improve air quality and reduce pollutants with the help of bio facade.

Knowledge, attitudes, and perception of air pollution in Ireland

AimAir pollution remains a major global public health challenge; and Ireland is no exception to the human health implications of exposure ambient air pollutants. Accurate and timely information can be critical to mitigate the harmful effects of air pollution. This study aimed to assess the knowledge, perceptions, and attitudes to poor air quality in Ireland to assist stakeholders in developing and implementing effective communication pieces and policies about the management of air pollution. Study designCross-sectional population-based cohort. MethodQuantitative data on knowledge, attitudes, and perceptions (KAP) were collected from respondents living across Ireland, and the results were analysed with SPSS (Version 28.0). ResultsAmong the 1005 respondents included in this study, the mean [SD] age was 46.1 [15.3] years; 53% were female (n = 530); and 66% and 35% of respondents were aware of air pollution and its adverse effects on health at a national and local level respectively (n = 668 and n = 353 respectively). In addition, there were significant relationships between socio-demographic and air pollution awareness. There were correlation between respondent’s age, gender, socio-economic group, and locality in Ireland. ConclusionThis study demonstrates that environmental health literacy around air pollution in critically lacking among respondents. Given that air pollution is an increasingly important global priority, opportunities need to create to improve reach and impact of communication of air quality health risk and mitigation measures.

An AQI decomposition ensemble model based on SSA-LSTM using improved AMSSA-VMD decomposition reconstruction technique

Air quality index (AQI) is a key index for monitoring air pollution and can be used as guide for ensuring good public health. Accurate AQI prediction allows timely control and management of air pollution. In this study, a new integrated learning model was constructed to predict AQI. A smart reverse learning approach based on AMSSA was utilized to increase the diversity of populations, and an improved AMSSA (IAMSSA) was established. The optimum parameters with penalty factor α and mode number K of VMD were obtained using IAMSSA. The IAMSSA-VMD was used to decompose nonlinear and non-stationary AQI information series into several regular and smooth sub-sequences. The Sparrow Search Algorithm (SSA) was used to determine the optimum LSTM parameters. The results showed that: (1) IAMSSA exhibits faster convergence and higher accuracy and stability using simulation experiments compared with seven conventional optimization algorithms in 12 test functions. (2) IAMSSA-VMD was used to decompose the original air quality data results in multiple uncoupled intrinsic mode function (IMF) components and one residual (RES). An SSA-LSTM model was built for each IMF and one RES component, which effectively extracted the predicted values. (3) LSTM, SSA-LSTM, VMD-LSTM, VMD-SSA-LSTM, AMSSA-VMD-SSA-LSTM, and IAMSSA-VMD-SSA-LSTM models were used for prediction of AQI based on data from three cities (Chengdu, Guangzhou, and Shenyang). IAMSSA-VMD-SSA-LSTM exhibited the optimal prediction performance with MAE, RMSE, MAPE, and R2 of 3.692, 4.909, 6.241, and 0.981, respectively. (4) Generalization outcomes revealed that the IAMSSA-VMD-SSA-LSTM model had optimal generalization ability. In summary, the decomposition ensemble model proposed in this study has higher prediction accuracy, improved fitting effect and generalization ability compared with other models. These properties indicate the superiority of the decomposition ensemble model and provides a theoretical and technical basis for prediction of air pollution and ecosystem restoration.

Health risk assessment from exposure to ambient VOCs and particulate matter in different functional zones in Dhanbad, India

This study evaluates the non-cancer risks (NCR) and cancer risks (CR) in outdoor working personnel in Dhanbad city, exposed to volatile organic compounds (VOCs) and particulate matter (PM) present in the ambient air. Dhanbad is known for its coal mines and is one of the most polluted cities in India and the world. Sampling was conducted in different functional zones; namely, traffic intersections, industrial, and institutional areas, to estimate the concentration of different PM-bound heavy metals and VOCs in the ambient air using Inductively coupled plasma-optical emission spectrometry (ICP-OES) and gas chromatography (GC) respectively. Our results show that the concentration levels (of VOCs and PM) and health risks were maximum at the traffic intersection area, followed by the industrial and institutional areas. The major contribution to CR came from chloroform, naphthalene, and PM-bound chromium, while the contribution to NCR mainly came from naphthalene, trichloroethylene, xylenes, and PM-bound chromium, nickel, and cadmium. It was observed that CR and NCR from VOCs are quite comparable (average CRvoc 8.92E-05 and NCRvoc 6.82) to that from the PM-bound heavy metals (average CRPM 9.93E-05 and NCRPM 3.52). According to the sensitivity analysis performed using Monte-Carlo Simulation, it was also found that the output risk is most significantly influenced by the pollutant concentration followed by exposure duration and exposure time. The study reveals that Dhanbad city is not only a critically polluted area but also a highly hazardous and cancer-prone area due to the pollution arising from incessant coal mining activities and heavy vehicular movement. Given the paucity of data on exposure to VOCs in ambient air and their risk assessment pertaining to coal mining cities of India, our study provides useful information and insight for the regulatory and enforcing authorities to devise appropriate strategies for air pollution and health risk management in such cities.

Firm size and pro-environmental behavior in Cameroon

Previous studies indicate a lack of analysis of pro-environmental behavior adoption in enterprises of various sizes. Very small enterprises, especially in the informal sector, have always been overlooked in the literature, although they are in the majority in most countries. This paper aims to examine the effects of firm size on pro-environmental behavior adoption in Cameroon. The analysis focuses on a sample of 141,926 firms drawn from the Second General Census of Enterprises (RGE-2) in Cameroon (NIS, 2018). The study adopted a statistical and econometrical approach based on the logit model. The results showed that the adoption of pro-environmental behavior increases with firm size. The probability of having a health, safety, and environment system increases by 16.70 points in large enterprises compared to 8.40 points in small enterprises. The probability of having a wastewater management system increases by 5.30 points in large enterprises compared to 2.30 points in small enterprises. The probability of having an air pollution management system increases by 2.20 points in large enterprises compared to 1.50 points in small enterprises. However, company size does not significantly influence the adoption of a solid waste management system. It is recommended to (i) raise awareness among large companies of the challenges of environmental protection and to strengthen controls on compliance with environmental standards, and (ii) to implement actions aimed at the migration of companies from the informal to the formal sector.

The perceptions and satisfaction index of Yogyakarta’s citizens on congestion and air pollution

Yogyakarta City is one of Indonesia's most popular tourist destinations. The tourism sector is the highest tax revenue for local government. Besides, tourism gives high revenue but also negatively impacts the citizens as hosts, such as congestion and pollution. This study was conducted to determine the importance level of each attribute on the quality of congestion and air pollution management services in Yogyakarta based on the citizen's perception by utilizing the Importance Performance Analysis (IPA) method. This study also aimed to determine the Yogyakarta citizens' satisfaction level with managing congestion and air pollution using the Customer Satisfaction Index (CSI) method. This study implemented a purposive sampling technique which was then obtained from the population sample of 100 respondents with 86 saturated samples. Based on the perception of the Yogyakarta citizens, The results showed that a high level of attributes included wide and comfortable pavements, trees that block the sunlight and have a cooling effect, motorists who obeyed traffic signs, and drivers who drive well. The Yogyakarta Citizens' satisfaction index result showed 60.54%, which has the category of "very poor".

A Spatio-Temporal Graph Convolutional Network for Air Quality Prediction

Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.

Long-Term Air Pollution Characteristics and Multi-scale Meteorological Factor Variability Analysis of Mega-mountain Cities in the Chengdu-Chongqing Economic Circle.

The online version contains supplementary material available at 10.1007/s11270-023-06279-8.

항만 대기질 개선을 위한 정책 우선순위 분석에 관한 연구

The port areas in the metropolitan area maintain higher concentrations of air pollution throughout the year compared to other port areas, so air pollution management is urgently needed. Air pollutant emissions from ports in the metropolitan area were analyzed to be more than twice the national average. Despite these circumstances, analyses and studies on the sustainable and feasible port air quality improvement policy measures for the port areas in the metropolitan area are insufficient. In this respect, the purpose of this study was to derive policy priorities for improving air quality in the metropolitan ports. As for the method of the study, the CFPR (Consistent Fuzzy Preference Relation) method, which can integrate and analyze the opinions of experts, was used.
 As a result of the analysis, in the overall weight ranking, the first, second, and third places were shown to be the strengthening of the ship fuel sulfur content standard, the expansion of eco-friendly ships, and the expansion of support for ship emission reduction devices, respectively. That is, all of the first, second, and third detailed factors were analyzed as policies related to vessel-oriented air pollutant reduction. In particular, most of the policy items ranked at the top were analyzed as policy factors that require strong regulatory policies, large-scale financial inputs, and government facility subsidy policies. This study has implications in that it presents useful policy measures that can be applied practically to policy makers to improve port air quality.

Assessment of Relationship Between Meteorological Parameters and Air Quality Index of Kota, Rajasthan, India

Meteorological parameters extremely influence the air quality of metropolitan cities worldwide. This study analyses the impact of meteorological variables on the quality of air for the Kota metropolis of Rajasthan (India) from 2018 to 2021 for four years. Wind speed and direction, relative humidity, precipitation, and temperature are the meteorological parameters selected to statistically determine the effect of meteorological variables on the AQI (air quality index). The AQIs are evaluated through PM10, PM2.5, NO2, and SO2. The average concentration of PM10 was 122.59 ± 44.11µg/m3, PM2.5 was 56.83 ± 24.89 µg/m3, NO2 was 24.91 ± 4.99 µg/m3, and SO2 was 7.39 ± 1.38 µg/m3 during the observation period. The average temperature varied between 18-38 °C in 2018, 15.7-34.8 °C in 2019, 15.5-35.1 in 2020, and 16.8-32.4 °C. Total rainfall in 2018, 2019, 2020, and 2021 was 685 mm, 1637 mm, 514 mm, and 1338 mm, respectively. The average wind speed in the city were 1.39, 1.30, 1.26, and 1.22 m/s in 2018, 2019, 2020, and 2021, respectively. The relative humidity ranged from 17-78 % in 2018, 22-84% in 2019, 40-90 % in 2020, and 24-82 % in 2021. The annual average AQI of Kota was 139, 118, 101, and 142 in 2018, 2019, 2020, and 2021, respectively. There is a statistically significant correlation between temperature and AQI (p<0.05), rainfall and AQI (p<0.05), and wind speed and AQI (p<0.05), which show a significant impact on the air quality of the Kota metropolis. At the same time, there is a statistically insignificant correlation between relative humidity and AQI (p>0.05), which shows an insignificant impact on air quality. The formulated equation for predicting AQI through meteorological parameters has wide scope utility in air pollution management and control.

Air pollutant prediction based on ARIMA-WOA-LSTM model

The problem of air pollution has always plagued people's lives, and the management of air pollution cannot be achieved without the prediction and assessment of the concentration of various pollutants. In this paper, we propose a method to accurately predict air pollutants with the aim of ensuring the efficiency of air pollution management. The proposed ARIMA-WOA-LSTM model uses ARIMA to extract the linear part of air pollution data and output the nonlinear part, while the WOA-LSTM model is used to predict the nonlinear part, where the whale algorithm is used to find the perfect hyperparameters for the LSTM, and the objectives of the search include the number of neurons, model learning rate and batch length. To prove the excellence of the model developed in the article ARIMA-WOA-LSTM is compared with ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, and LSTM. The results show that ARIMA-WOA-LSTM performs better than other models in three aspects: pollutant prediction accuracy, model prediction accuracy, and prediction stability; the combined model also performs much better than the single model in the three aspects; The whale algorithm is excellent for the search of the five hyperparameters in the LSTM, which is important for the error reduction of the model. ARIMA-WOA-LSTM model has high reference for air pollution management.

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere.The purpose of this study was to create the best O3model for forecasting O3concentrations in the industrial area and to determine the variables that affect O3concentrations.Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3concentration were developed.MLR3had the highest correlation coefficient of 0.792 during development as compared to models MLR1and MLR2. MLR2was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models.The establishment of an O3prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions.

The Role of Intersection Geometry in Urban Air Pollution Management

Air pollution represents one of the most complex problems of humanity. Traffic contributes significantly to this by emitting large amounts of harmful gases. This problem is particularly pronounced at urban intersections due to frequent changes in vehicle movement dynamics. This paper primarily presents the influence of intersection geometry on pollutant emissions levels. In addition, the influence of various traffic policies promoting greater use of public transport and zero-emission vehicles is also examined. The research combines the field part of recording existing intersections in Sarajevo, Bosnia and Herzegovina with traffic microsimulation. Detailed data on vehicles’ movements were obtained by advanced video processing using the DataFromSky tool, while the PTV Vissim 2022 and Bosch ESTM (2022) software were used to simulate traffic and estimate emissions at geometrically different intersections. The results showed that, in saturated traffic conditions, signalized intersections cause up to 50% lower emissions compared with two-lane and turbo roundabouts and that the impact of the geometric change is more significant than the impact of zero-emission vehicles. In unsaturated conditions, the differences in emissions at different intersections are negligible, with the highest reductions in pollution achieved by using zero-emission vehicles.

Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options

Many of the current atmospheric environmental problems facing Thailand are linked to air pollution that is largely derived from biomass burning. Different parts of Thailand have distinctive sources of biomass emissions that affect air quality. The main contributors to atmospheric particulate matter (PM), especially the PM2.5 fraction in Thailand, were highlighted in a recent study of PM derived from biomass burning. This review is divided into six sections. Section one is an introduction to biomass burning in Thailand. Section two covers issues related to biomass burning for each of the four main regions in Thailand, including Northern, Northeastern, Central, and Southern Thailand. In northern Thailand, forest fires and the burning of crop residues have contributed to air quality in the past decade. The northeast region is mainly affected by the burning of agricultural residues. However, the main contributor to PM in the Bangkok Metropolitan Region is motor vehicles and crop burning. In Southern Thailand, the impact of agoindustries, biomass combustion, and possible agricultural residue burning are the primary sources, and cross-border pollution is also important. The third section concerns the effect of biomass burning on human health. Finally, perspectives, new challenges, and policy recommendations are made concerning improving air quality in Thailand, e.g., forest fuel management and biomass utilization. The overall conclusions point to issues that will have a long-term impact on achieving a blue sky over Thailand through the development of coherent policies and the management of air pollution and sharing this knowledge with a broader audience.