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Fulltext Effects of Population Weighting on PM10 Concentration Estimation

Abdul Shakor AS, Pahrol MA, Mazeli MI

J Environ Public Health, 2020;2020:1561823.
PMID: 32351580 DOI: 10.1155/2020/1561823

Particulate matter with an aerodynamic diameter of 10 μm or less (PM10) pollution poses a considerable threat to human health, and the first step in quantifying health impacts of human exposure to PM10 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provide a more refined exposure assessment as it includes the spatiotemporal distribution of the population into the pollution concentration estimation. This study assessed the population weighting effects on the estimated PM10 concentrations in Malaysia for years 2000, 2008, and 2013. Estimated PM10 annual mean concentrations with a spatial resolution of 5 kilometres retrieved from satellite data and population count obtained from the Gridded Population of the World version 4 (GPWv4) from the Centre for International Earth Science Information Network (CIESIN) were overlaid to generate the PWEL of PM10 for each state. The calculated PWEL of PM10 concentrations were then classified based on the World Health Organization (WHO) and the national Air Quality Guidelines (AQG) and interim targets (IT) for comparison. Results revealed that the annual mean PM10 concentrations in Malaysia ranged from 31 to 73 µg/m3 but became generally lower, ranging from 20 to 72 µg/m3 after population weighting, suggesting that the PM10 population exposure in Malaysia might have been overestimated. PWEL of PM10 distribution showed that the majority of the population lived in areas that complied with the national AQG, but were vulnerable to exposure level 3 according to the WHO AQG and IT, indicating that the population was nevertheless potentially exposed to significant health effects from long-term exposure to PM10 pollution.

Matched MeSH terms: Particulate Matter/analysis*; Particulate Matter/chemistry
Fulltext Cluster Analysis Evaluating PM2.5, Occupation Risk and Mode of Transportation as Surrogates for Air-pollution and the Impact on Lung Cancer Diagnosis and 1-Year Mortality

Abdul Wahab S, Hassan A, Latif MT, Vadiveel Y, Jeyabalan T, Soo CI, et al.

Asian Pac J Cancer Prev, 2019 07 01;20(7):1959-1965.
PMID: 31350951 DOI: 10.31557/APJCP.2019.20.7.1959

Objective: Epidemiological studies have reported the close relationship between risk for lung cancers and air pollution
in particular, for non-smoking related lung cancers. However, most studies used residential address as proxies which may
not estimate accurately an individual’s air pollution exposure. Therefore, the aim of this study was to identify risk factors
such as occupation and mode of transportation associated with lung cancer diagnosis and death. Methods: Subjects
with lung cancer (n=514) were evaluated both by chart reviews for clinical data and interviews to determine residential
address for ten years, main occupation and main mode of transportation. Annual particulate matter with diameter size
less than 2.5 micrometre (PM2.5) concentration were calculated based on particulate matter with diameter size less than
10 micrometre (PM10) data recorded by Malaysian Department of Environment. Logistic regression analysis, cluster
analysis and the Cox regression analysis were performed to the studied variables. Results: This study concurred with
previous studies that lung adenocarcinoma were diagnosed in predominantly younger, female non-smokers compared
to the other types of lung cancers. Lung adenocarcinoma subjects had annual PM2.5 that was almost twice higher than
squamous cell carcinoma, small cell carcinoma and other histological subtypes (p=0.024). Independent of smoking,
the κ -means cluster analysis revealed two clusters in which the high risk cluster involves occupation risk with air
pollution of more than four hours per day, main transportation involving motorcycle and trucks and mean annual PM2.5
concentration of more than 30 based on residential address for more than ten years. The increased risk for the high-risk
cluster was more than five times for the diagnosis of lung adenocarcinoma (OR=5.69, 95% CI=3.14-7.21, p<0.001).
The hazard ratio for the high-risk cluster was 3.89 (95% CI=2.12-4.56, p=0.02) for lung adenocarcinoma mortality at
1 year. Conclusion: High-risk cluster including PM2.5, occupation risk and mode of transportation as surrogates for
air-pollution exposure was identified and highly associated with lung adenocarcinoma diagnosis and 1-year mortality.

Matched MeSH terms: Particulate Matter/adverse effects*
Classification of air quality using fuzzy synthetic multiplication

Abdullah L, Khalid ND

Environ Monit Assess, 2012 Nov;184(11):6957-65.
PMID: 22160435 DOI: 10.1007/s10661-011-2472-1

Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.

Matched MeSH terms: Particulate Matter/analysis
Correlation between hotspots and air quality in Pekanbaru, Riau, Indonesia in 2006-2007

Adelin Anwar, Liew J, Mohd Talib Latif, Mohamed Rozali Othman

Sains Malaysiana, 2010;39:169-174.

Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.

Matched MeSH terms: Particulate Matter
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
Air pollution effects on adult mortality rate in developing countries

Ahmad NA, Ismail NW, Ahmad Sidique SF, Mazlan NS

Environ Sci Pollut Res Int, 2021 Feb;28(7):8709-8721.
PMID: 33068244 DOI: 10.1007/s11356-020-11191-3

Although industrialisation is a crucial aspect of economic growth across developing nations, through the release of air contaminants, industrial activities may also create adverse environmental health consequences. Noting that continuous production and other economic activities are crucial for continued survival, this study explores this issue by including the role of governance that is deemed essential but the literature is relatively sparse particularly in the context of developing countries. This research empirically analyses the relationship between air pollution and adult mortality rates from 72 developing countries from the period of 2010 until 2017. Particulate matter (PM2.5) and carbon dioxide (CO2) are used as indicators of air pollution. From the generalized method of moments (GMM) estimations, the results reveal that air pollution negatively affects adult mortality rate. The result reveals that a 10% increase in the PM2.5 level induces the adult mortality rates to increase between 0.04% and 0.06%. In addition, the government significantly moderates the negative effect of air pollution on adult mortality, whereby a one-unit enhancement in governance quality index reduces mortality among the adults in the developing countries by 0.01%. On the other hand, CO2 emission also appears to be positive, but not statistically significant. The results suggest that governance and public health interplay in the sense of a transition towards economic development for improved living and health states can be achievable with improved governance quality.

Matched MeSH terms: Particulate Matter
Air pollution, governance quality, and health outcomes: evidence from developing countries

Ahmad NA, Ismail NW, Sidique SFA, Mazlan NS

Environ Sci Pollut Res Int, 2023 Mar;30(14):41060-41072.
PMID: 36630041 DOI: 10.1007/s11356-023-25183-6

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.

Matched MeSH terms: Particulate Matter/analysis
Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms

AlThuwaynee OF, Kim SW, Najemaden MA, Aydda A, Balogun AL, Fayyadh MM, et al.

Environ Sci Pollut Res Int, 2021 Aug;28(32):43544-43566.
PMID: 33834339 DOI: 10.1007/s11356-021-13255-4

This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).

Matched MeSH terms: Particulate Matter
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
Fulltext Exploring the linkage between PM2.5 levels and COVID-19 spread and its implications for socio-economic circles

Ali SM, Malik F, Anjum MS, Siddiqui GF, Anwar MN, Lam SS, et al.

Environ Res, 2021 02;193:110421.
PMID: 33160973 DOI: 10.1016/j.envres.2020.110421

A pneumonia-like disease of unknown origin caused a catastrophe in Wuhan city, China. This disease spread to 215 countries affecting a wide range of people. World health organization (WHO) called it a pandemic and it was officially named as Severe Acute Respiratory Syndrome Corona virus 2 (SARS CoV-2), also known as Corona virus disease (COVID-19). This pandemic compelled countries to enforce a socio-economic lockdown to prevent its widespread. This paper focuses on how the particulate matter pollution was reduced during the lockdown period (23 March to April 15, 2020) as compared to before lockdown. Both ground-based and satellite observations were used to identify the improvement in air quality of Pakistan with primary focus on four major cities of Lahore, Islamabad, Karachi and Peshawar. Both datasets have shown a substantial reduction in PM2.5 pollution levels (ranging from 13% to 33% in case of satellite observations, while 23%-58% in ground-based observations) across Pakistan. Result shows a higher rate of COVID-19 spread in major cities of Pakistan with poor air quality conditions. Yet more research is needed in order to establish linkage between COVID-19 spread and air pollution. However, it can be partially attributed to both higher rate of population density and frequent exposure of population to enhanced levels of PM2.5 concentrations before lockdown period.

Matched MeSH terms: Particulate Matter/analysis
The effects of air pollution on human mortality: does gender difference matter in African countries?

Aliyu AJ, Ismail NW

Environ Sci Pollut Res Int, 2016 Nov;23(21):21288-21298.
PMID: 27497851

The relationship between environmental factors and human health has long been a concern among academic researchers. We use two indicators of environmental pollution, namely particulate matter (PM10) and carbon dioxide (CO2) to examine the effects of poor air quality on human mortality. This study explores an issue that has largely been ignored, particularly in the African literature, where the effect of air pollution on human mortality could be influenced by gender specification. We analyse a panel data from 35 African countries and our result suggests that the elevated levels of PM10 and CO2 have a significant effect on the increasing mortality rates in infants, under-five children and adults. Although the effect of poor air quality on adults is found to differ between genders, such difference is not statistically significant. We conclude that the air pollution effects, on average, are similar between genders in the African countries.

Matched MeSH terms: Particulate Matter/analysis; Particulate Matter/pharmacology
Relationship between vehicle count and particulate air pollution in Amman, Jordan

Alnawaiseh NA, Hashim JH, Isa ZM

Asia Pac J Public Health, 2015 Mar;27(2):NP1742-51.
PMID: 22899706 DOI: 10.1177/1010539512455046

The main objective of this cross-sectional comparative study is to observe the relationship between traffic-related air pollutants, particularly particulate matter (PM) of total suspended particulate (TSP) and PM of size 10 µm (PM10), and vehicle traffic in Amman, Jordan. Two study areas were chosen randomly as a high-polluted area (HPA) and low-polluted area (LPA). The findings indicate that TSP and PM10 were still significantly correlated with traffic count even after controlling for confounding factors (temperature, relative humidity, and wind speed): TSP, r = 0.726, P < .001; PM10, r = 0.719, P < .001). There was a significant positive relationship between traffic count and PM level: TSP, P < .001; PM10, P < .001. Moreover, there was a significant negative relationship between temperature and PM10 level (P = .018). Traffic volume contributed greatly to high concentrations of TSP and PM10 in areas with high traffic count, in addition to the effect of temperature.

Matched MeSH terms: Particulate Matter/analysis*
Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature

Althuwaynee OF, Pokharel B, Aydda A, Balogun AL, Kim SW, Park HJ

J Expo Sci Environ Epidemiol, 2021 07;31(4):709-726.
PMID: 33159165 DOI: 10.1038/s41370-020-00271-8

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Matched MeSH terms: 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 Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE)

Andersen ZJ, Pedersen M, Weinmayr G, Stafoggia M, Galassi C, Jørgensen JT, et al.

Neuro-oncology, 2018 02 19;20(3):420-432.
PMID: 29016987 DOI: 10.1093/neuonc/nox163

Background: Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent.
Methods: In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5-10 μm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.
Results: Of 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89-3.14 per 10-5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38-2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors.
Conclusion: We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors.

Matched MeSH terms: Particulate Matter/adverse effects*
Applied chemometric approach in identification sources of air quality pattern in Selangor, Malaysia

Ang KH

Sains Malaysiana, 2018;47:471-479.

In recent years, Malaysia has experienced quite a few number of chronic air pollution problems and it has become a
major contributor to the deterioration of human health and ecosystems. This study aimed to assess the air quality data
and identify the pattern of air pollution sources using chemometric analysis through hierarchical cluster analysis (HCA),
discriminant analysis (DA), principal component analysis (PCA) and multiple linear regression analysis (MLR). The air
quality data from January 2016 until December 2016 was obtained from the Department of Environment Malaysia. Air
quality data from eight sampling stations in Selangor include the selected variables of nitrogen dioxide (NO2
), ozone (O3
),
sulfur dioxide (SO2
), carbon monoxide (CO) and particulate matter (PM10). The HCA resulted in three clusters, namely low
pollution source (LPS), moderate pollution source (MPS) and slightly high pollution source (SHPS). Meanwhile, DA resulted
in two and four variables for the forward stepwise mode and the backward stepwise mode, respectively. Through PCA,
it was identified that the main pollutants of LPS, MPS and SHPS came from industrial and vehicle emissions, agricultural
systems, residential factors and natural emission sources. Among the three models yielded from the MLR analysis, it was
found that SHPS is the most suitable model to be used for the prediction of Air Pollution Index. This study concluded that
a clearer review and practical design of air quality monitoring network would be beneficial for better management of
air pollution. The study also suggested that chemometric techniques have the ability to show significant information on
spatial variability for large and complex air quality data.

Matched MeSH terms: Particulate Matter
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
Correlation Between Hotspots and Air Quality in Pekanbaru-Riau, Indonesia in 2006-2007

Anwar A, Liew J, Othman M, Latif M

Sains Malaysiana, 2010;39:169-174.

Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.

Matched MeSH terms: Particulate Matter
Fulltext Long-term exposure to outdoor and household air pollution and blood pressure in the Prospective Urban and Rural Epidemiological (PURE) study

Arku RE, Brauer M, Ahmed SH, AlHabib KF, Avezum Á, Bo J, et al.

Environ Pollut, 2020 Jul;262:114197.
PMID: 32146361 DOI: 10.1016/j.envpol.2020.114197

Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35-70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5

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

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