Search

Abstract Cities in mainland Southeast Asia have long faced smog problems. Developing effective control strategies for smog reduction has been a challenge for academics and policy makers. In this study, emission control measures were assessed using both the Weather Research and Forecasting (WRF) and Community Multi-scale Air Quality (CMAQ) modeling systems during a smog episode between 1st and 31st of March 2012. The study found that there was a strong impact from biomass burning outside of the city on PM concentrations within the city, with contributions of approximately 85% and 89% for PM10 and PM2.5 respectively. Control of biomass burning in the city significantly affects local PM concentrations. This study investigated the success of biomass burning control inside the city when no biomass is burned outside of the city. The control scenarios focused on specific reduction levels in biomass burning emissions, which included 0%, 20%, 40%, 60%, 80% and 100%. The results yielded the following rates: the mean PM2.5 concentrations decreased by 0.70% per %decrease of biomass burning emission; the maximum daily average PM2.5 concentrations decreased by 0.84% per %decrease of biomass burning emission; and the average percentage of PM2.5 concentrations that exceeded the NAAQS decreased by 1.05% per %decrease of biomass burning emission. This information provide good guidelines for successful decision-making at each control level to solve the problems related to of smog in cities. However, smog problems cannot only be solved by emission control measures in the city; biomass burning must also be reduced on a regional scale. Graphical abstract Image 1 Highlights • This study reveals the impact of biomass burning emission sources inside and outside Phayao city. • The success of different control levels for limiting biomass burning to lower the PM concentrations are investigated. • Most of the PM in cities in mainland Southeast Asia are attributed to biomass burning emissions from regional sources. [ABSTRACT FROM AUTHOR]

Severe particulate matter problems are known to occur frequently in mainland Southeast Asia (MSEA) during times of intensified biomass burning (BB). Therefore, the objectives of this study are to investigate BB characteristics and their impacts on PM10 concentrations in MSEA during a smog episode that took place in March 2012. According to the analysis of our study, PM10 was emitted from BB and accounted for 72% of all sources. Using the WRF-CMAQ modeling system, the current study showed that severe PM10 levels in MSEA exceeded the World Health Organization (WHO) ambient air quality standard (AAQS) recommended safe level of air quality by 51%. Subsequently, based on aggregated monthly data, the area average PM10 concentration was found to be 80 μg/m3 over MSEA. In addition, the model showed that the major source of particulate matter was from BB and contributed to 49% of ambient PM10 concentrations in MSEA while it specifically contributed 73%, 69%, 59%, 45%, 33%, and 31% in Laos, Myanmar, Cambodia, Thailand, China, and Vietnam, respectively. Based on a sensitivity analysis, we found that every 1 Tg of PM10 increased in MSEA would result in a 27.8 μg/m3 increase in PM10 concentrations in MSEA. However, these research findings tend to vary from one country to another, where for every 1 Tg of PM10 increased, it would result in a 60.6, 50.8, 17.6, 14.1, 13.0, and 9.9 μg/m3 increase in PM10 concentrations in Laos, Myanmar, Thailand, China, Cambodia, and Vietnam, respectively. These findings assume great significance those who wish to establish PM10 control strategies at both regional and national levels, given that BB needs to be recognized as the primary genesis of PM10 problems. Image 1 • A case study was conducted during the peak biomass burning month from 2011 to 2019. • Biomass burning contributed 72% of PM10 emission in MSEA. • Biomass burning contributed 49% of ambient PM10 concentrations in MSEA. • The largest influence from biomass burning was found in Laos and Myanmar. [ABSTRACT FROM AUTHOR]

This study investigated the effects of weather conditions, air pollutants, and the air quality index (AQI) on daily cases of COVID-19 in the Bangkok Metropolitan Region (BMR). In this research, we collected data from January 1 to March 30, 2020 (90 days). This study used secondary data of meteorological and air pollutant parameters obtained from the Pollution Control Department of the Ministry of Natural Resources and Environment as well as daily confirmed COVID-19 case data in the BMR obtained from the official webpage of the Department of Disease Control, Ministry of Public Health, Thailand. We employed descriptive statistics, and Spearman and Kendall rank correlation tests were used to investigate the associations of weather variables, air pollutants, AQI with daily confirmed COVID-19 cases. Our findings indicate that CO, NO 2 , SO 2 , O 3 PM 10 , PM 2.5 , AQI have a significantly negative association with daily confirmed COVID-19 cases in the BMR, whereas meteorological parameters such as temperature, relative humidity (RH), absolute humidity (AH) and wind speed (WS) showed significant positive associations with daily confirmed COVID-19 cases in the BMR. Our study is a useful supplement to encourage regulatory bodies to promote environmental strategies, as air pollution regulation could be a sustainable policy for mitigating the harmful effects of air pollutants. Furthermore, this study provides new insights into the relationship between daily meteorological factors, AQI, and air pollutants and daily confirmed COVID-19 cases in the BMR. These data may provide useful information to the public health authorities and decision makers in Thailand, as well as to the World Health Organization (WHO), in order to set proper strategic aimed at reducing the impact of the COVID-19. Future studies concerning SARS-CoV-2 and other viruses should investigate the possibility of infectious droplet dispersion in indoor and outdoor air during and after the epidemic outbreak. • The NO levels and AQI decreased during the outbreak. • SO 2 , NO 2 , CO, PM 2.5 , PM 10 , and O 3 rose by 42.16%, 39.02%, 23.00%, 18.43%, 3.17%, and 0.02%, respectively. • The temperature, RH, AH and WS showed positive significant associations with daily confirmed COVID-19 cases. • The daily confirmed COVID-19 cases displayed negative associations with CO, NO 2 , SO 2 , O 3 , PM 10 , PM 2.5 , and AQI. [ABSTRACT FROM AUTHOR]