Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some1inspirations for air pollution forecast in other countries with complex terrain.
This article proposes a novel data selection technique called the mixed peak-over-threshold-block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.
This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall's τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events.
Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM10). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.
In Asian countries such as China, Malaysia, Pakistan, India, Taiwan, Korea, Japan and Hong Kong, ambient air total suspended particulates and PM2.5 concentration data were collected and discussed during the years of 1998-2015 in this study. The aim of the present study was to (1) investigate and collect ambient air total suspended particulates (TSP) and PM2.5 concentrations for Asian countries during the past two decades. (2) Discuss, analyze and compare those particulates (TSP and PM2.5) annual concentration distribution trends among those Asian countries during the past two decades. (3) Test the mean concentration differences in TSP and PM2.5 among the Asian countries during the past decades. The results indicated that the mean TSP concentration order was shown as China > Malaysia > Pakistan > India > Taiwan > Korea > Japan. In addition, the mean PM2.5 concentration order was shown as Vietnam > India > China > Hong Kong > Mongolia > Korea > Taiwan > Japan and the average percentages of PM2.5 concentrations for Taiwan, China, Japan, Korea, Hong Kong, Mongolia and Other (India and Vietnam) were 8, 21, 6, 8, 14, 13 and 30%, respectively, during the past two decades. Moreover, t test results revealed that there were significant mean TSP and PM2.5 concentration differences for either China or India to any of the countries such as Taiwan, Korea and Japan in Asia during the past two decades for this study. Noteworthy, China and India are both occupied more than 60% of the TSP and PM2.5 particulates concentrations out of all the Asia countries. As for Taiwan, the average PM2.5 concentration displayed increasing trend in the years of 1998-1999. However, it showed decreasing trend in the years of 2000-2010. As for Korea, the average PM2.5 concentrations showed decreasing trend during the years of 2001-2013. Finally, the average PM2.5 concentrations for Mongolia displayed increasing trend in the years of 2004-2013.
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
While studies have demonstrated that air pollution can be catastrophic to the population's health, few empirical studies are found in the economic literature because a considerable proportion of the evidence comes from epidemiological studies. Because of the crucial role of governance in the health community, good governance has been a contentious issue in public sector management in recent years. Therefore, the aim of this study is to examine the effects of air pollution and the role of governance on health outcomes. This study employed the generalized method of moment (GMM) estimation techniques to analyse panel data for 72 developing countries from 2010 to 2017. The empirical results confirm that higher PM2.5 and CO2 levels have a detrimental influence on life expectancy and healthy life expectancy, whereas the role of governance has a positive impact on life expectancy and healthy life expectancy. Furthermore, the findings show governance quality plays a role in moderating the negative effect of PM2.5 on health outcomes. The ongoing rise in air pollution has had a significant impact on the health of developing countries. It appears that governance quality has improved health outcomes. The findings have important policy implications, such that strengthening governance can reduce air pollution emissions in developing countries. However, to reduce the health effects of air pollution, developing countries must implement effective environmental development policies and track the implementation and enforcement of such policies.
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.
Road transport contributes over 70% of air pollution in urban areas and is the second largest contributor to the total carbon dioxide emissions in Malaysia at 21% in 2016. Transport-related air pollutants (TRAPs) such as NOx, SO2, CO and particulate matter (PM) pose significant threats to the urban population's health. Malaysia has targeted to deploy 885,000 EV cars on the road by 2030 in the Low Carbon Mobility Blueprint (LCMB). This study aims to quantify the health co-benefits of electric vehicle adoption from their impacts on air quality in Malaysia. Two EV uptake projections, i.e. LCMB and Revised EV Adoption (REVA) projections, and five electricity generation mix scenarios were modelled up to 2040. We used comparative health risk assessment to estimate the potential changes in mortality and burden of diseases (BoD) from the emissions in each scenario. Intake fractions and exposure-risk functions were used to calculate the burden from respiratory diseases (PM2.5, NOx, SO2, CO), cardiovascular diseases and lung cancer (PM2.5). Results showed that along with a net reduction of carbon emissions across all scenarios, there could be reduced respiratory mortality from NOx by 10,200 mortality (176,200 DALYs) and SO2 by 2600 mortality (45,400 DALYs) per year in 2040. However, there could also be additional 719 mortality (9900 DALYs) per year from PM2.5 and 329 mortality (5600 DALYs) from CO per year. The scale of reduction in mortality and BoD from NOx and SO2 are significantly larger than the scale of increase from PM2.5 and CO, indicating potential net positive health impacts from the EV adoption in the scenarios. The health cost savings from the reduced BoD of respiratory mortality could reach up to RM 7.5 billion per year in 2040. In conclusion, EV is a way forward in promoting a healthy and sustainable future transport in Malaysia.
Environmental issues such as environmental pollutions and climate change are the impacts of globalization and become debatable issues among academics and industry key players. One of the environmental issues which is air pollution has been catching attention among industrialists, researchers, and communities around the world. However, it has always neglected until the impacts on human health become worse, and at times, irreversible. Human exposure to air pollutant such as particulate matters, sulfur dioxide, ozone and carbon monoxide contributed to adverse health hazards which result in respiratory diseases, cardiorespiratory diseases, cancers, and worst, can lead to death. This has led to a spike increase of hospitalization and emergency department visits especially at areas with worse pollution cases that seriously impacting human life and health. To address this alarming issue, a predictive model of air pollution is crucial in assessing the impacts of health due to air pollution. It is also critical in predicting the air quality index when assessing the risk contributed by air pollutant exposure. Hence, this systemic review explores the existing studies on anticipating air quality impact to human health using the advancement of Artificial Intelligence (AI). From the extensive review, we highlighted research gaps in this field that are worth to inquire. Our study proposes to develop an AI-based integrated environmental and health impact assessment system using federated learning. This is specifically aims to identify the association of health impact and pollution based on socio-economic activities and predict the Air Quality Index (AQI) for impact assessment. The output of the system will be utilized for hospitals and healthcare services management and planning. The proposed solution is expected to accommodate the needs of the critical and prioritization of sensitive group of publics during pollution seasons. Our finding will bring positive impacts to the society in terms of improved healthcare services quality, environmental and health sustainability. The findings are beneficial to local authorities either in healthcare or environmental monitoring institutions especially in the developing countries.
Traffic-Related Air Pollution (TRAP) exposure has been connected to significant health impacts among children. A cross-sectional comparative study was conducted among school children in Malaysia to determine the relationship between their exposure to TRAP and respiratory health effects. Air monitoring was conducted in schools and residences, while the children's routines were investigated using a diary of daily activities. Respondents' background and respiratory symptoms were obtained from a validated questionnaire, while a spirometry test was performed to determine their lung function status. The distances between schools and residences from the had contributed to the higher concentration of air pollutants in this study, which had associations with the children's respiratory symptoms and lung function status. PM2.5 was the main predictor influencing the respondents' respiratory symptoms and lung function abnormalities. In conclusion, exposure of school children to a high TRAP level might increase their risk of getting respiratory symptoms and lung function reduction.
Atmospheric Pollution due to airborne particle is a major concern to many cities in the Southeast Asian region, including Kuala Lumpur. Within the last six years air particulate samples have been collected from a site in Kuala Lumpur and measured for their PM10, PM2.5 and elemental concentrations. The results showed that the daily PM10 (
An integrated source apportionment methodology is developed by amalgamating the receptor-oriented model (ROM) and source-oriented numerical simulations (SOM) together to eliminate the weaknesses of individual SA methods. This approach attempts to apportion and dissect the PM2.5 sources in the Yangtze River Delta region during winter. First, three ROM models (CMB, PMF, ME2) are applied and compared for the preliminary SA results, with information from PM2.5 sampling and lab analysis during the winter seasons. The detailed source category contribution of SOM to PM2.5 is further simulated using the WRF-CAMx model. The two pieces of information from both ROM and SOM are then stitched together to give a comprehensive information on the PM2.5 sources over the region. With the integrated approach, the detailed contributing sources of the ambient PM2.5 at different receptors including rural and urban, coastal and in-land, northern and southern receptors are analyzed. The results are compared with previous data and shows good agreement. This integrative approach is more comprehensive and is able to produce a more profound and detailed understanding between the sources and receptors, compared with single models.
A systematic literature review was conducted to summarize the overall thermal performance of different gasified cooking stoves from the available literature. For this purpose, available studies from the last 14 years (2008 to 2022) were searched using different search strings. After screening, a total of 28 articles were selected for this literature review. Scopus, Google Scholar, and Web of Science databases were used as search strings by applying "Gasifier cooking stove" AND "producer gas cooking stove" AND "thermal performance" keywords. This review uncovers different gasified cooking stoves, cooking fuels, and fabrication materials besides overall thermal performances. The result shows that the overall thermal performance of different gasified cooking stoves was 5.88% to 91% depending on the design and burning fuels. The premixed producer gas burner with a swirl vane stove provided the highest overall thermal performance range, which was 84% to 91%, and the updraft gasified stove provided the lowest performance, which was 5.88% to 8.79%. The result also demonstrates that the wood pellets cooking fuel provided the highest thermal performance and corn straw briquette fuel provided the lowest for gasified cooking stoves. The overall thermal performance of wood pellets was 38.5% and corn straw briquette was 10.86%.
Nowadays, PM2.5 concentrations greatly influence indoor air quality in subways and threaten passenger and staff health because PM2.5 not only contains heavy metal elements, but can also carry toxic and harmful substances due to its small size and large specific surface area. Exploring the physicochemical and distribution characteristics of PM2.5 in subways is necessary to limit its concentration and remove it. At present, there are numerous studies on PM2.5 in subways around the world, yet, there is no comprehensive and well-organized review available on this topic. This paper reviews the nearly twenty years of research and over 130 published studies on PM2.5 in subway stations, including aspects such as concentration levels and their influencing factors, physicochemical properties, sources, impacts on health, and mitigation measures. Although many determinants of station PM2.5 concentration have been reported in current studies, e.g., the season, outdoor environment, and station depth, their relative influence is uncertain. The sources of subway PM2.5 include those from the exterior (e.g., road traffic and fuel oil) and the interior (e.g., steel wheels and rails and metallic brake pads), but the proportion of these sources is also unknown. Control strategies of PM mainly include adequate ventilation and filtration, but these measures are often inefficient in removing PM2.5. The impacts of PM2.5 from subways on human health are still poorly understood. Further research should focus on long-term data collection, influencing factors, the mechanism of health impacts, and PM2.5 standards or regulations.
Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R2) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R2 value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).
The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
In recent years, ozone pollution in China has been shown to increase in frequency and persistence despite the concentrations of fine particulate matter (PM2.5) decreasing steadily. Open crop straw burning (OCSB) activities are extensive in China and emit large amounts of trace gases during a short period that could lead to elevated ozone concentrations. This study addresses the impacts of OCSB emissions on ground-level ozone concentration and the associated health impact in China. Total VOCs and NOx emissions from OCSB in 2018 were 798.8 Gg and 80.6 Gg, respectively, with high emissions in Northeast China (31.7%) and North China (23.7%). Based on simulations conducted for 2018, OCSB emissions are estimated to contribute up to 0.95 µg/m3 increase in annual averaged maximum daily 8-hour (MDA8) ozone and up to 1.35 µg/m3 for the ozone season average. The significant impact of OCSB emissions on ozone is mainly characterized by localized and episodic (e.g., daily) changes in ozone concentration, up to 20 µg/m3 in North China and Yangtze River Delta region and even more in Northeast China during the burning season. With the implementation of straw burning bans, VOCs and NOx emissions from OCSB dropped substantially by 46.9%, particularly over YRD (76%) and North China (60%). Consequently, reduced OCSB emissions result in an overall decrease in annual averaged MDA8 ozone, and reductions in monthly MDA8 ozone could be over 10 µg/m3 in North China. The number of avoided premature death due to reduced OCSB emissions (considering both PM2.5 and ozone) is estimated to be 6120 (95% Confidence Interval: 5320-6800), with most health benefits gained over east and central China. Our results illustrate the effectiveness of straw burning bans in reducing ozone concentrations at annual and national scales and the substantial ozone impacts from OCSB events at localized and episodic scales.
A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.
This study aimed to assess the association of exposure to particle-bound (PM2.5) polycyclic aromatic hydrocarbons (PAHs) with potential genotoxicity and cancer risk among children living near the petrochemical industry and comparative populations in Malaysia. PM2.5 samples were collected using a low-volume sampler for 24 h at three primary schools located within 5 km of the industrial area and three comparative schools more than 20 km away from any industrial activity. A gas chromatography-mass spectrometer was used to determine the analysis of 16 United States Environmental Protection Agency (USEPA) priority PAHs. A total of 205 children were randomly selected to assess the DNA damage in buccal cells, employing the comet assay. Total PAHs measured in exposed and comparative schools varied, respectively, from 61.60 to 64.64 ng m-3 and from 5.93 to 35.06 ng m-3. The PAH emission in exposed schools was contributed mainly by traffic and industrial emissions, dependent on the source apportionment. The 95th percentiles of the incremental lifetime cancer risk estimated using Monte Carlo simulation revealed that the inhalation risk for the exposed children and comparative populations was 2.22 × 10-6 and 2.95 × 10-7, respectively. The degree of DNA injury was substantially more severe among the exposed children relative to the comparative community. This study reveals that higher exposure to PAHs increases the risk of genotoxic effects and cancer among children.