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Associations between air pollutants and peak expiratory flow and fractional exhaled nitric oxide in students

Adman MA, Hashim JH, Manaf MRA, Norback D

Int J Tuberc Lung Dis, 2020 02 01;24(2):189-195.
PMID: 32127103 DOI: 10.5588/ijtld.19.0096

BACKGROUND: Studies on the effects of outdoor air pollution on the respiratory health of students in tropical countries such as Malaysia are limited.OBJECTIVE: To assess associations between outdoor air pollutants and peak expiratory flow (PEF) and fractional exhaled nitric oxide (FeNO).METHOD: PEF and FeNO levels of 487 students recruited in Melaka and Putrajaya, Malaysia, were measured in April and June 2014. Multiple linear regression with mutual adjustment was used to analyse the associations between exposure to air pollution and health.RESULTS: PEF was significantly associated with ozone for 1-day exposure (β = -13.3 l/min, 95% CI -22.7 to -3.8), carbon monoxide for 2-day exposure (β = -57.2 l/min, 95% CI -90.7 to -23.7) and particulate matter ≦10 μm in diameter for 3-day exposure (β = -6.0 l/min, 95% CI -9.2 to -2.8) and 7-day exposure (β = -8.6 l/min, 95% CI -13.0 to -4.1). Stratified analysis showed that associations between PEF and outdoor air pollutant exposures were similar in students with and without elevated FeNO levels.CONCLUSION: Outdoor air pollution in Malaysia may cause airway obstruction unrelated to eosinophilic airway inflammation among students as measured using FeNO.

Matched MeSH terms: Particulate Matter/adverse effects; Particulate Matter/analysis
Fulltext Chronic airflow obstruction and ambient particulate air pollution

Amaral AFS, Burney PGJ, Patel J, Minelli C, Mejza F, Mannino DM, et al.

Thorax, 2021 12;76(12):1236-1241.
PMID: 33975927 DOI: 10.1136/thoraxjnl-2020-216223

Smoking is the most well-established cause of chronic airflow obstruction (CAO) but particulate air pollution and poverty have also been implicated. We regressed sex-specific prevalence of CAO from 41 Burden of Obstructive Lung Disease study sites against smoking prevalence from the same study, the gross national income per capita and the local annual mean level of ambient particulate matter (PM2.5) using negative binomial regression. The prevalence of CAO was not independently associated with PM2.5 but was strongly associated with smoking and was also associated with poverty. Strengthening tobacco control and improved understanding of the link between CAO and poverty should be prioritised.

Matched MeSH terms: Particulate Matter/analysis; Particulate Matter/toxicity
Fulltext The Influence of Traffic-Related Air Pollution (TRAP) in Primary Schools and Residential Proximity to Traffic Sources on Histone H3 Level in Selected Malaysian Children

Suhaimi NF, Jalaludin J, Abu Bakar S

Int J Environ Res Public Health, 2021 07 28;18(15).
PMID: 34360284 DOI: 10.3390/ijerph18157995

This study aimed to investigate the association between traffic-related air pollution (TRAP) exposure and histone H3 modification among school children in high-traffic (HT) and low-traffic (LT) areas in Malaysia. Respondents' background information and personal exposure to traffic sources were obtained from questionnaires distributed to randomly selected school children. Real-time monitoring instruments were used for 6-h measurements of PM10, PM2.5, PM1, NO2, SO2, O3, CO, and total volatile organic compounds (TVOC). Meanwhile, 24-h measurements of PM2.5-bound black carbon (BC) were performed using air sampling pumps. The salivary histone H3 level was captured using an enzyme-linked immunosorbent assay (ELISA). HT schools had significantly higher PM10, PM2.5, PM1, BC, NO2, SO2, O3, CO, and TVOC than LT schools, all at p < 0.001. Children in the HT area were more likely to get higher histone H3 levels (z = -5.13). There were positive weak correlations between histone H3 level and concentrations of NO2 (r = 0.37), CO (r = 0.36), PM1 (r = 0.35), PM2.5 (r = 0.34), SO2 (r = 0.34), PM10 (r = 0.33), O3 (r = 0.33), TVOC (r = 0.25), and BC (r = 0.19). Overall, this study proposes the possible role of histone H3 modification in interpreting the effects of TRAP exposure via non-genotoxic mechanisms.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Chemical, Biological and Morphological Properties of Fine Particles during Local Rice Straw Burning Activities

Ramli NA, Md Yusof NFF, Zarkasi KZ, Suroto A

Int J Environ Res Public Health, 2021 08 02;18(15).
PMID: 34360485 DOI: 10.3390/ijerph18158192

Rice straw is commonly burned openly after harvesting in Malaysia and many other Asian countries where rice is the main crop. This operation emits a significant amount of air pollution, which can have severe consequences for indoor air quality, public health, and climate change. Therefore, this study focuses on determining the compositions of trace elements and the morphological properties of fine particles. Furthermore, the species of bacteria found in bioaerosol from rice burning activities were discovered in this study. For morphological observation of fine particles, FESEM-EDX was used in this study. Two main categories of particles were found, which were natural particles and anthropogenic particles. The zinc element was found during the morphological observation and was assumed to come from the fertilizer used by the farmers. ICP-OES identifies the concentration of trace elements in the fine particle samples. A cultured method was used in this study by using nutrient agar. From this study, several bacteria were identified: Exiguobavterium indicum, Bacillus amyloliquefaciens, Desulfonema limicola str. Jadabusan, Exiguobacterium acetylicum, Lysinibacillus macrolides, and Bacillus proteolyticus. This study is important, especially for human health, and further research on the biological composition of aerosols should be conducted to understand the effect of microorganisms on human health.

Matched MeSH terms: Particulate Matter/analysis
Fulltext A two-year assessment of particulate air pollution and sources in Kuwait

Alahmad B, Al-Hemoud A, Kang CM, Almarri F, Kommula V, Wolfson JM, et al.

Environ Pollut, 2021 Aug 01;282:117016.
PMID: 33848912 DOI: 10.1016/j.envpol.2021.117016

BACKGROUND: Kuwait and the Gulf region have a desert, hyper-arid and hot climate that makes outdoor air sampling challenging. The region is also affected by intense dust storms. Monitoring challenges from the harsh climate have limited data needed to inform appropriate regulatory actions to address air pollution in the region.
OBJECTIVES: To compare gravimetric measurements with existing networks that rely on beta-attenuation measurements in a desert climate; determine the annual levels of PM2.5 and PM10 over a two-year period in Kuwait; assess compliance with air quality standards; and identify and quantify PM2.5 sources.
METHODS: We custom-designed particle samplers that can withstand large quantities of dust without their inlet becoming overloaded. The samplers were placed in two populated residential locations, one in Kuwait City and another near industrial and petrochemical facilities in Ali Sabah Al-Salem (ASAS) to collect PM2.5 and PM10 samples for mass and elemental analysis. We used positive matrix factorization to identify PM2.5 sources and apportion their contributions.
RESULTS: We collected 2339 samples during the period October 2017 through October 2019. The beta-attenuation method in measuring PM2.5 consistently exceeded gravimetric measurements, especially during dust events. The annual levels for PM2.5 in Kuwait City and ASAS were 41.6 ± 29.0 and 47.5 ± 27.6 μg/m3, respectively. Annual PM2.5 levels in Kuwait were nearly four times higher than the U.S. National Ambient Air Quality Standard. Regional pollution was a major contributor to PM2.5 levels in both locations accounting for 44% in Kuwait City and 46% in ASAS. Dust storms and re-suspended road dust were the second and third largest contributors to PM2.5, respectively.
CONCLUSIONS: The premise that frequent and extreme dust storms make air quality regulation futile is dubious. In this comprehensive particulate pollution analysis, we show that the sizeable regional anthropogenic particulate sources warrant national and regional mitigation strategies to ensure compliance with air quality standards.

Matched MeSH terms: Particulate Matter/analysis
Modeling the transition behaviors of PM10 pollution index

Masseran N, Safari MAM

Environ Monit Assess, 2020 Jun 17;192(7):441.
PMID: 32557137 DOI: 10.1007/s10661-020-08376-1

Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study

Chaudhary V, Bhadola P, Kaushik A, Khalid M, Furukawa H, Khosla A

Sci Rep, 2022 07 28;12(1):12949.
PMID: 35902653 DOI: 10.1038/s41598-022-16781-4

Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH3) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM2.5, PM10, NH3, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value 0.60) and PM10 (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH3 (r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.

Matched MeSH terms: Particulate Matter/analysis
Seasonal variation and size distribution of inorganic and carbonaceous components, source identification of size-fractioned urban air particles in Kuala Lumpur, Malaysia

Jamhari AA, Latif MT, Wahab MIA, Hassan H, Othman M, Abd Hamid HH, et al.

Chemosphere, 2022 Jan;287(Pt 4):132309.
PMID: 34601373 DOI: 10.1016/j.chemosphere.2021.132309

This study aims to determine the inorganic and carbonaceous components depending on the seasonal variation and size distribution of urban air particles in Kuala Lumpur. Different fractions of particulate matter (PM) were measured using a Nanosampler from 17 February 2017 until 27 November 2017. The water-soluble inorganic ions (WSIIs) and carbonaceous components in all samples were analysed using ion chromatography and carbon analyser thermal/optical reflectance, respectively. Total PM concentration reached its peak during the southwest (SW) season (70.99 ± 6.04 μg/m3), and the greatest accumulation were observed at PM0.5-1.0 (22%-30%, 9.55 ± 1.03 μg/m3) and PM2.5-10 (22%-25%, 10.34 ± 0.81 μg/m3). SO42-, NO3- and NH4+ were major contributors of WSIIs, and their formation was favoured mainly during SW season (80.5% of total ions). PM0.5-1.0 and PM2.5-10 exhibited the highest percentage of WSII size distribution, accounted for 28.4% and 13.5% of the total mass, respectively. The average contribution of carbonaceous species (OC + EC) to total carbonaceous concentrations were higher in PM0.5-1.0 (35.2%) and PM2.5-10 (26.6%). Ultrafine particles (PM<0.1) consistently indicated that the sources were from vehicle emission while the SW season was constantly dominated by biomass burning sources. Using the positive matrix factorization (PMF) model, secondary inorganic aerosol and biomass burning (30.3%) was known as a significant source of overall PM. As a conclusion, ratio and source apportionment indicate the mixture of biomass burning, secondary inorganic aerosols and motor vehicle contributed to the size-segregated PM and seasonal variation of inorganic and carbonaceous components of urban air particles.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Effect of large-scale social restriction (PSBB) during COVID-19 on outdoor air quality: Evidence from five cities in DKI Jakarta Province, Indonesia

Anugerah AR, Muttaqin PS, Purnama DA

Environ Res, 2021 06;197:111164.
PMID: 33872645 DOI: 10.1016/j.envres.2021.111164

The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM10, SO2, CO, O3, and NO2, from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO2 (7.5%) and then SO2 (5.7%). However, we unexpectedly found that during lockdown, the PM10 concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.

Comput Intell Neurosci, 2022;2022:9755422.
PMID: 36531923 DOI: 10.1155/2022/9755422

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

Matched MeSH terms: Particulate Matter/analysis
Underestimation of biomass burning contribution to PM2.5 due to its chemical degradation based on hourly measurements of organic tracers: A case study in the Yangtze River Delta (YRD) region, China

Li Q, Zhang K, Li R, Yang L, Yi Y, Liu Z, et al.

Sci Total Environ, 2023 May 10;872:162071.
PMID: 36775179 DOI: 10.1016/j.scitotenv.2023.162071

Biomass burning (BB) has significant impacts on air quality and climate change, especially during harvest seasons. In previous studies, levoglucosan was frequently used for the calculation of BB contribution to PM2.5, however, the degradation of levoglucosan (Lev) could lead to large uncertainties. To quantify the influence of the degradation of Lev on the contribution of BB to PM2.5, PM2.5-bound biomass burning-derived markers were measured in Changzhou from November 2020 to March 2021 using the thermal desorption aerosol gas chromatography-mass spectrometry (TAG-GC/MS) system. Temporal variations of three anhydro-sugar BB tracers (e.g., levoglucosan, mannosan (Man), and galactosan (Gal)) were obtained. During the sampling period, the degradation level of air mass (x) was 0.13, indicating that ~87 % of levoglucosan had degraded before sampling in Changzhou. Without considering the degradation of levoglucosan in the atmosphere, the contribution of BB to OC were 7.8 %, 10.2 %, and 9.3 % in the clean period, BB period, and whole period, respectively, which were 2.4-2.6 times lower than those (20.8 %-25.9 %) considered levoglucosan degradation. This illustrated that the relative contribution of BB to OC could be underestimated (~14.9 %) without considering degradation of levoglucosan. Compared to the traditional method (i.e., only using K+ as BB tracer), organic tracers (Lev, Man, Gal) were put into the Positive Matrix Factorization (PMF) model in this study. With the addition of BB organic tracers and replaced K+ with K+BB (the water-soluble potassium produced by biomass burning), the overall contribution of BB to PM2.5 was enhanced by 3.2 % after accounting for levoglucosan degradation based on the PMF analysis. This study provides useful information to better understand the effect of biomass burning on the air quality in the Yangtze River Delta region.

Matched MeSH terms: Particulate Matter/analysis
Fulltext On the investigation of COVID-19 lockdown influence on air pollution concentration: regional investigation over eighteen provinces in Iraq

Hashim BM, Al-Naseri SK, Al Maliki A, Sa'adi Z, Malik A, Yaseen ZM

Environ Sci Pollut Res Int, 2021 Sep;28(36):50344-50362.
PMID: 33956319 DOI: 10.1007/s11356-021-13812-x

At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.

Matched MeSH terms: Particulate Matter/analysis
Trends in atmospheric particulate matter in Dhaka, Bangladesh, and the vicinity

Rana MM, Sulaiman N, Sivertsen B, Khan MF, Nasreen S

Environ Sci Pollut Res Int, 2016 Sep;23(17):17393-403.
PMID: 27230142 DOI: 10.1007/s11356-016-6950-4

Dhaka and its neighboring areas suffer from severe air pollution, especially during dry season (November-April). We investigated temporal and directional variations in particulate matter (PM) concentrations in Dhaka, Gazipur, and Narayanganj from October 2012 to March 2015 to understand different aspects of PM concentrations and possible sources of high pollution in this region. Ninety-six-hour backward trajectories for the whole dry season were also computed to investigate incursion of long-range pollution into this area. We found yearly PM10 concentrations in this area about three times and yearly PM2.5 concentrations about six times greater than the national standards of Bangladesh. Dhaka and its vicinity experienced several air pollution episodes in dry season when PM2.5 concentrations were 8-13 times greater than the World Health Organization (WHO) guideline value. Higher pollution and great contribution of PM2.5 most of the time were associated with the north-westerly wind. Winter (November to January) was found as the most polluted season in this area, when average PM10 concentrations in Dhaka, Gazipur, and Narayanganj were 257.1, 240.3, and 327.4 μg m(-3), respectively. Pollution levels during wet season (May-October) were, although found legitimate as per the national standards of Bangladesh, exceeded WHO guideline value in 50 % of the days of that season. Trans-boundary source identifications using concentration-weighted trajectory method revealed that the sources in the eastern Indian region bordering Bangladesh, in the north-eastern Indian region bordering Nepal and in Nepal and its neighboring areas had high probability of contributing to the PM pollutions at Gazipur station.

Matched MeSH terms: Particulate Matter/analysis*
GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Tella A, Balogun AL

Environ Sci Pollut Res Int, 2022 Dec;29(57):86109-86125.
PMID: 34533750 DOI: 10.1007/s11356-021-16150-0

Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.

Matched MeSH terms: Particulate Matter/analysis
Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products

Sakti AD, Anggraini TS, Ihsan KTN, Misra P, Trang NTQ, Pradhan B, et al.

Sci Total Environ, 2023 Jan 01;854:158825.
PMID: 36116660 DOI: 10.1016/j.scitotenv.2022.158825

Air pollution has massive impacts on human life and poor air quality results in three million deaths annually. Air pollution can result from natural causes, including volcanic eruptions and extreme droughts, or human activities, including motor vehicle emissions, industry, and the burning of farmland and forests. Emission sources emit multiple pollutant types with diverse characteristics and impacts. However, there has been little research on the risk of multiple air pollutants; thus, it is difficult to identify multi-pollutant mitigation processes, particularly in Southeast Asia, where air pollution moves dynamically across national borders. In this study, the main objective was to develop a multi-air pollution risk index product for CO, NO2, and SO2 based on Sentinel-5P remote sensing data from 2019 to 2020. The risk index was developed by integrating hazard, vulnerability, and exposure analyses. Hazard analysis considers air pollution data from remote sensing, vulnerability analysis considers the air pollution sources, and exposure analysis considers the population density. The novelty of this study lies in its development of a multi-risk model that considers the weights obtained from the relationship between the hazard and vulnerability parameters. The highest air pollution risk index values were observed in urban areas, with a high exposure index that originates from pollution caused by human activity. Multi-risk analysis of the three air pollutants revealed that Singapore, Vietnam, and the Philippines had the largest percentages of high-risk areas, while Indonesia had the largest total high-risk area (4361 km2). Using the findings of this study, the patterns and characteristics of the risk distribution of multiple air pollutants in Southeast Asia can be identified, which can be used to mitigate multi-pollutant sources, particularly with respect to supporting the clean air targets in the Sustainable Development Goals.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

Ravindiran G, Hayder G, Kanagarathinam K, Alagumalai A, Sonne C

Chemosphere, 2023 Oct;338:139518.
PMID: 37454985 DOI: 10.1016/j.chemosphere.2023.139518

Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.

Matched MeSH terms: Particulate Matter/analysis
Smart solutions for clean air: An AI-guided approach to sustainable industrial pollution control in coal-fired power plant

Lim JY, Teng SY, How BS, Loy ACM, Heo S, Jansen J, et al.

Environ Pollut, 2023 Oct 15;335:122335.
PMID: 37558197 DOI: 10.1016/j.envpol.2023.122335

Conventional fossil fuels are relied on heavily to meet the ever-increasing demand for energy required by human activities. However, their usage generates significant air pollutant emissions, such as NOx, SOx, and particulate matter. As a result, a complete air pollutant control system is necessary. However, the intensive operation of such systems is expected to cause deterioration and reduce their efficiency. Therefore, this study evaluates the current air pollutant control configuration of a coal-powered plant and proposes an upgraded system. Using a year-long dataset of air pollutants collected at 30-min intervals from the plant's telemonitoring system, untreated flue gas was reconstructed with a variational autoencoder. Subsequently, a superstructure model with various technology options for treating NOx, SOx, and particulate matter was developed. The most sustainable configuration, which included reburning, desulfurization with seawater, and dry electrostatic precipitator, was identified using an artificial intelligence (AI) model to meet economic, environmental, and reliability targets. Finally, the proposed system was evaluated using a Monte Carlo simulation to assess various scenarios with tightened discharge limits. The untreated flue gas was then evaluated using the most sustainable air pollutant control configuration, which demonstrated a total annual cost, environmental quality index, and reliability indices of 44.1 × 106 USD/year, 0.67, and 0.87, respectively.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm

Masood A, Hameed MM, Srivastava A, Pham QB, Ahmad K, Razali SFM, et al.

Sci Rep, 2023 Nov 29;13(1):21057.
PMID: 38030733 DOI: 10.1038/s41598-023-47492-z

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Matched MeSH terms: Particulate Matter/analysis
Recent progress on phytoremediation of urban air pollution

Duan X, Gu H, Lam SS, Sonne C, Lu W, Li H, et al.

Chemosphere, 2024 Feb;349:140821.
PMID: 38042424 DOI: 10.1016/j.chemosphere.2023.140821

The rapid growth of population and economy has led to an increase in urban air pollutants, greenhouse gases, energy shortages, environmental degradation, and species extinction, all of which affect ecosystems, biodiversity, and human health. Atmospheric pollution sources are divided into direct and indirect pollutants. Through analysis of the sources of pollutants, the self-functioning of different plants can be utilized to purify the air quality more effectively. Here, we explore the absorption of greenhouse gases and particulate matter in cities as well as the reduction of urban temperatures by plants based on international scientific literature on plant air pollution mitigation, according to the adsorption, dust retention, and transpiration functions of plants. At the same time, it can also reduce the occurrence of extreme weather. It is necessary to select suitable tree species for planting according to different plant functions and environmental needs. In the context of tight urban land use, the combination of vertical greening and urban architecture, through the rational use of plants, has comprehensively addressed urban air pollution. In the future, in urban construction, attention should be paid to the use of heavy plants and the protection and development of green spaces. Our review provides necessary references for future urban planning and research.

Matched MeSH terms: Particulate Matter/analysis
Fulltext Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, et al.

Environ Int, 2023 May;175:107931.
PMID: 37119651 DOI: 10.1016/j.envint.2023.107931

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.

Matched MeSH terms: Particulate Matter/analysis

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