A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand (original) (raw)
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BMC Public Health
Background The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM10, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM10 prediction for different seasons using meteorological factors. Method The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM10 concentration were then developed, namely annual, summer, rainy, and winter seasons. Results The maximum daily PM10 concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal ...
Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model
Advances in Meteorology
As a result of considerable changes in rural areas in Northern Thailand, the frequency and intensity of haze outbreaks from particulate pollution, particularly fine particulate matter (PM2.5), has increased in this region. To supplement ground-based monitoring where PM2.5 observation is limited, this study applied a multivariate linear regression model to predict PM2.5 concentrations in 2020 using aerosol optical depth (AOD); meteorological parameters of wind velocity, temperature, and relative humidity; and gaseous pollutants such as SO2, NO2, CO, and O3 from ground-based measurements at three locations: Chiang Mai, Lampang, and Nan provinces in Northern Thailand. Two multivariate linear regression models were conducted in this study. The first model (model 1) is a generic model with meteorological parameters of aerosol optical depth (AOD), temperature, relative humidity, and wind speed. The second model (model 2) includes meteorological parameters and several gaseous pollutants, s...
2022
Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly P...
Geomatics, Natural Hazards and Risk
In an era of rapidly changing climate, investigating the impacts of climate parameters on major air pollutants such as Particulate matter (PM 10) is imperative to mitigate its adverse effect. This study utilizes Geographic Information System (GIS), a multivariate regression model (MVR) and Pearson correlation analysis to examine the interrelationship between PM 10 and major climate parameters such as temperature, wind speed, and humidity. Although the application of MVR for predicting PM10 has been examined in previous studies, however, the spatial modelling and prediction of this air pollutant is limited. Accurate spatial assessment of pollutants' hazard susceptibility in relation to climate change can accelerate mitigation initiatives. Thus, to understand the behavior, seasonal pattern, and trend of PM 10 concentration which is vital for good air quality, GIS is essential for enhanced visualization and interpretation of the predicted occurrence of the pollutant. The acquired data were randomly divided into 80% and 20% for training and validation of the MVR model, respectively while GIS was used to model the spatial distribution of the predicted ambient PM10 concentration, highlighting the hotspots of future PM 10 hazard. A positive correlation index was obtained between PM 10 with temperature and wind speed. However, humidity showed a negative correlation. The regression model showed high predictive performance of R 2 ¼ 0.298, RMSE ¼ 12.737, and MAE of 10.343, with the highest PM 10 concentration correlated with the warming event in the southwest monsoon. Temperature, wind speed, and humidity were identified as the most critical variables influencing PM 10 concentration in the study area, in descending order of importance. This study's outcome provides valuable spatio-temporal information on future climate change impact on PM 10 in the study area with the potential to support effective air quality management.
SN Applied Sciences, 2020
Satellite-retrieved aerosol optical depth essentially provides an economical option for regular monitoring of particulate matter (PM) concentration; however, the constrains and challenges come in terms of estimation accuracy. In the present study, we estimated PM 2.5 and PM 10 (PM of aerodynamic diameter lesser than 2.5, 10 µm, respectively) for 11 sites in Bangladesh using different methods. Univariate model showed destitute performance (R 2 < 0.1), whereas integrating MODIS-AOD with surface meteorology, multivariate models enhanced accuracy (R 2 > 0.6); meanwhile, radial kernel-based 'eps'-type support vector regression model outperformed rest (R 2 > 0.8). Furthermore, we investigated variations in ground concentration of PM 2.5 , PM 10 during 2013-2018 and found annual mean concentration of 76.34 ± 34.12 µg m −3 and 136.25 ± 68.94 µg m −3 , respectively. Predominant anthropogenic contribution to elevated pollution is well remarked by PM 2.5 /PM 10 ratio, highest during January (0.65 ± 0.06) and lowest during July (0.48 ± 0.11).
PM2.5 Pollutant in Asia—A Comparison of Metropolis Cities in Indonesia and Taiwan
International Journal of Environmental Research and Public Health, 2019
Air pollution has emerged as a significant health, environmental, economic, and social problem all over the world. In this study, geospatial technologies coupled with a LUR (Land Use Regression) approach were applied to assess the spatial-temporal distribution of fine particulate (PM2.5). In-situ observations of air pollutants from ground monitoring stations from 2016–2018 were used as dependent variables, while the land-use/land cover, a NDVI (Normalized Difference Vegetation Index) from a MODIS sensors, and meteorology data allocations surrounding the monitoring stations from 0.25–5 km buffer ranges were collected as spatial predictors from GIS and remote sensing databases. A linear regression method was developed for the LUR model and 10-fold cross-validation was used to assess the model robustness. The R2 model obtained was 56% for DKI Jakarta, Indonesia, and 83% for Taipei Metropolis, Taiwan. According to the results of the PM2.5 model, the essential predictors for DKI Jakarta ...
Atmospheric Environment, 2007
The space/time distribution of PM 10 in Thailand is modeled using the Bayesian maximum entropy (BME) method of modern spatiotemporal geostatistics. Three kinds of BME spatiotemporal maps over Thailand are sought on the most polluted day for each year of a 6-year period from 1998 to 2003. These three maps are (1) the map of the BME estimate of daily PM 10 , (2) the map of the associated BME prediction error, and (3) the BME non-attainment map showing areas where the BME estimate does not attain a 68% probability of meeting the ambient standard for PM 10 . These detailed space/time PM 10 maps provide invaluable information for decision-makers in air quality management. Knowing accurately the spatiotemporal distribution of PM 10 is necessary to develop and evaluate strategies used to abate PM 10 levels. The space/time BME estimate of PM 10 on the worst day of the year offers a general picture as to where daily PM 10 levels are not in compliance with the air-quality standard. Delineating these areas leads to the BME non-attainment maps, which are useful in identifying unhealthy zones, where sensitive population such as asthmatic children, seniors, or those with cardiopulmonary disease should be advised to avoid outdoor activities. The results of the space/time BME analysis of PM 10 are further extended to assess whether the current monitoring network is adequate. The current distribution of monitoring stations can be evaluated by combining the available demographic information with the BME estimation error maps. Administrative districts with large population size and high BME normalized estimation error are suggested as the target for adding new monitoring stations. r
Scientific Reports
Surface PM2.5 concentrations are required for exposure assessment studies. Remotely sensed Aerosol Optical Depth (AOD) has been used to derive PM2.5 where ground data is unavailable. However, two key challenges in estimating surface PM2.5 from AOD using statistical models are (i) Satellite data gaps, and (ii) spatio-temporal variability in AOD-PM2.5 relationships. In this study, we estimated spatially continuous (0.03° × 0.03°) daily surface PM2.5 concentrations using MAIAC AOD over Madhya Pradesh (MP), central India for 2018 and 2019, and validated our results against surface measurements. Daily MAIAC AOD gaps were filled using MERRA-2 AOD. Imputed AOD together with MERRA-2 meteorology and land use information were then used to develop a linear mixed effect (LME) model. Finally, a geographically weighted regression was developed using the LME output to capture spatial variability in AOD-PM2.5 relationship. Final Cross-Validation (CV) correlation coefficient, r2, between modelled an...
Estimation of particulate matter from visibility in Bangkok, Thailand
Journal of Exposure Analysis and Environmental Epidemiology, 2001
Lack of daily data on airborne particles has been a common problem in an air pollution research. To deal with this problem, a regression model was developed to estimate daily PM10 concentration using visibility in Bangkok from 1992 to 1997, based on 1092 visibility / PM10 pair-observations on low humidity days (humidity 76.5%). Visibility was significantly and inversely associated with PM10 (r = 0.71) , after adjusting for minimum temperature and winter indicator variable. The R 2 of the model was 0.51.
Particulate matter is the most prevailing pollutant in Peninsular Malaysia having the highest API value compared to the other criteria pollutants. Long-term exposure to small particles less than 10 micrometres may lead to a marked reduction in life expectancy due to increase cardio-pulmonary and lung cancer mortality. Effective forecasting models at the local level predict the concentrations of particulate matter is crucial as the information generated allows the authority and people within a community to take precautionary measures to avoid exposure to unhealthy levels of air quality and implement strategic measures that improve air quality status. The aim of this study is to establish MLR models for different monsoon seasons with meteorological factors as predictors. Daily observations of PM10 concentrations in Kuala Terengganu, Malaysia from January 2005 to December 2011 were selected for predicting PM10 concentration level. The MLR models for NEM, Inter Monsoon 1, SWM and Inter Monsoon 2 disclose R2 of 0.68, 0.58, 0.57, and 0.63, respectively. Wind speed, relative humidity and rainfall exhibit negative relationship whilst temperature and atmospheric pressure are directly correlated with PM10 concentrations. In conclusion, the developed MLR models are appropriate for forecasting PM10 concentrations at local level for each monsoon.