Spatial Correlation Analysis between Particulate Matter 10 (PM10) Hazard and Respiratory Diseases in Chiang Mai Province, Thailand

Trang, Nguyen Ha; Tripathi, Nimisha

doi:10.5194/isprsarchives-xl-8-185-2014

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…Air pollution's contribution to mortality was greater in Bangkok, than in Hong Kong, Shanghai and Wuhan in China ( Taneepanichskul et al, 2018 ; Wong et al, 2008 ). Short-term studies in Thailand also found associations between air pollution and respiratory and cardiovascular mortality ( Ostro et al, 1999 ; Taneepanichskul et al, 2018 ) and morbidity ( Buadong et al, 2009 ; Phosri et al, 2019 ; Trang and Tripathi, 2014 ).…”

Section: Introductionmentioning

confidence: 91%

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS)

Paoin

Ueda

Ingviya

et al. 2021

Environmental Research

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Background Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand. Methods This longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ≤10 μm (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005–2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM 10 , O 3 , NO 2 , SO 2 , and CO. Results After controlling for potential confounders, we found that an IQR increase in PM 10 was significantly associated with self-reported high blood pressure (HR = 1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR = 1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR = 1.05, 95%CI: 0.91, 1.21). SO 2 was also positively associated with self-reported high blood pressure (HR = 1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR = 1.20, 95%CI: 1.11, 1.30), and diabetes (HR = 1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR = 1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O 3 and NO 2 . Conclusions Long-term exposure to air pollution, especially for PM 10 and SO 2 was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases car...

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Section: Introductionmentioning

confidence: 91%

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS)

Paoin

Ueda

Ingviya

et al. 2021

Environmental Research

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“…PM 10 is an important component of air-pollution monitoring networks since it affects human health by causing respiratory problems (Balakrishnan et al, 2002;Pope et al, 2011a, b;Ha Trang and Tripathi, 2014), while inhaling PM 10 , and most particularly Particulate Matter < 2.5 μm (PM 2.5 ), can cause cardiovascular diseases (Dominici et al, 2006), birth defects and premature death (Ballester et al, 2010). PM 10 , as aerosol particles, can also affect the climate system by scattering and absorbing the incoming solar radiation resulting in heating and/or cooling effects (Ramanathan and Carmichael, 2008) depending on the type of particles (i.e.…”

Section: Introductionmentioning

confidence: 99%

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

Zaman

Kanniah

Kaskaoutis

2017

Atmospheric Research

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The insufficient number of ground-based stations for measuring Particulate Matter < 10 μm (PM 10) in the developing countries hinders PM 10 monitoring at a regional scale. The present study aims to develop empirical models for PM 10 estimation from space over Malaysia using aerosol optical depth (AOD 550) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM 10 estimation. The model development and training are performed via comparison with measured PM 10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R 2 = 0.71 and RMSE = 11.61 μg m − 3 compared to the MLR method (R 2 = 0.66 and RMSE = 12.39 μg m − 3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD 550 is the most important parameter for PM 10 estimations (R 2 = 0.59 and RMSE = 13.61 μg m − 3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R 2 = 0.66, RMSE = 12.39 μg m − 3). The estimated PM 10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R 2 = 0.58, RMSE = 10.16 μg m − 3) and MLR technique (R 2 = 0.56, RMSE = 10.58 μg m − 3). The significant accuracy that has been attained in PM 10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM 10 distribution at large spatial and temporal scales.

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“…Spatial distributions of PM10 during February-April were higher than during January and May, and was peak in March, especially in the northern part of Chiang Mai connected to Chiang Rai and near Myanmar, neighbouring country which faced to the same haze episode. The PM10 hazard map almost dominated in the north areas of Chiang Mai through 4 weeks of March 2007 14 . Furthermore, the PM10 concentration in this period was higher than the limit set by the Thai national ambient air quality standards (120 µg/m 3 ) especially in March 2013.…”

Section: Distribution Of Pm10 and Its Correlation With Climate Factorsmentioning

confidence: 99%

Geospatial analysis of relationship between climate factors and diffusion of air pollution in Chiang Mai, Thailand

Nakapan¹,

Choopun²

2018

ScienceAsia

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Geospatial analysis is applied to find a relationship between climate factors (relative humidity and temperature) and air pollutants (PM10 and CO) from January to May 2012-2014, the period when Chiang Mai province, northern part of Thailand, was covered with haze episode. Air pollutants and climate factors data were obtained from air monitoring stations of the Pollution Control Department. Temporal and spatial distribution of air pollutants and climate factors were analysed by Inverse Distance Weight techniques in ARCGIS. Correlation between air pollutants (PM10 and CO) and climate factors (relative humidity and temperature) were also investigated. The results showed that temporal trend of PM10 and CO were similar, they were high during February to April. Low level of relative humidity was observed during February to April, while high temperature was found in March and April. When the relative humidity decreased, the PM10 increased especially in the northern part of Chiang Mai with high correlation (R 2 = 0.83, 0.66, 0.73). On the other hand, when high moisture (%RH) covered almost Chiang Mai area during January 2012 and May 2013, the PM10 was decreased, and low correlation between the PM10 and the temperature was observed (R 2 = 0.01, 0.01, 0.04). Distribution of CO covered in the area during March and April was similar to PM10, however, it was higher in the central and the northern part of Chiang Mai. High correlation between CO and relative humidity was found (R 2 = 0.90, 0.02, 0.92), whereas low relationship between CO and temperature was observed.

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Spatial Correlation Analysis between Particulate Matter 10 (PM10) Hazard and Respiratory Diseases in Chiang Mai Province, Thailand

Cited by 15 publications

References 6 publications

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS)

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS)

Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

Geospatial analysis of relationship between climate factors and diffusion of air pollution in Chiang Mai, Thailand

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