Hospital out-and-in-patients as Functions of Trace Gaseous Species and Other Meteorological Parameters in Chiang-Mai, Thailand

Abstract

The aims of this study were to investigate the impact of meteorological parameters and trace gas concentrations on daily hospital walk-ins and admissions in Chiang-Mai province, Thailand, during 2007Thailand, during -2013. Advanced statistical models, including t-tests, Analysis of Variance (ANOVA), Multiple Linear Regression Analysis (MLRA) and Incremental Lifetime Particulate Matter Exposure (ILPE), were constructed using meteorological data from the Pollution Control Department (PCD), Ministry of Natural Resources and Environment (MNRE), Thailand, and the Thai Meteorological Department at Chiang-Mai Province Air Quality Observatory Site (TMCS) as well as the number of walk-in and admitted patients at Nakornping Hospital, Chiang-Mai (NHCM). The results showed that all trace gaseous species and PM 10 were significantly higher during the "haze episode" than during the "non-haze period." The FTIR spectra highlight the relatively homogeneous organic functional compositions of PM 2.5 collected from urban, suburban and rural observatory sites, indicating that agricultural waste burning plays an important role in air quality during the "haze episode." The effect of age on susceptibility to respiratory diseases was investigated by separating the dataset into four groups (i.e., < 15 years, 15-59 years, 60-74 years and ≥ 75 years). The ANOVA results revealed a significant increase in hospital walk-ins and admissions for both genders in the < 15 years group (p < 0.005). MLRA revealed the significantly highest impacts of CO on hospital walk-ins for both genders. The predicted ILPE of PM 10 showed the highest values for both genders during the "haze-episode" in 2007, with average values of 3.338 ± 0.576 g and 1.838 ± 0.317 g for male and female outdoor workers, respectively, over an exposure duration of 25 years.

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

“…Several other time series studies of air pollutants and hospital admissions have been conducted in Thailand. Pongpiachan and Paowa [46] examined gaseous air pollutants and in-and outpatients for respiratory disease in Chiang Mai over 2007-2013 and found the largest positive association with CO (PM was not analysed). Pothirat et al [47] examined admissions in an open-air facility in Chiang Mai province due to cardiovascular and respiratory diseases over 2016-2017; the dataset used was likely a subset of that in the present study.…”

“…In our study, we used the 90th and 95th percentiles of PM 10 concentrations (i.e., 87.1 μg/m 3 and 109.6 μg/m 3 , respectively) to identify days of biomass burning exposure, which is slightly lower than the 99 percentile employed by Morgan et al [35]. A range of other approaches also have been employed to identify days with exposure to burning, or haze, including a doubling of total suspended particulates (mean = 56.9 μg/m 3 [49]), the extent of discoloration in the sky [36], a threshold of 80 μg/m 3 to indicate 'unhealthy' levels [50], and exposure in the month of March [46]. Other studies of exposure to fires have employed software to track polluted air mass trajectories based on meteorological data (e.g., [51]).…”

Nitrogen Dioxide (NO2) Level Changes during the Control of COVID-19 Pandemic in Thailand

“…In urban areas, the non-CO 2 air pollutants such as nitrogen oxides (NO x ), carbon monoxide (CO), methane (CH 4 ), sulfur dioxide (SO 2 ), and volatile organic compounds (VOCs), typically arise due to exposure from the transportation sector (ChooChuay et al, 2020a;ChooChuay et al, 2020b;ChooChuay et al, 2020c;Karner et al, 2010;Pongpiachan and Iijima, 2016;Pongpiachan et al, 2017). Transportation-related air emissions play a major role for heat-trapping in atmospheric layers (IPCC, 2014;United Nations, 1992;Yue and Gao, 2018), while agricultural waste burning is another important source of air pollution (Pongpiachan, 2016;Castellanos et al, 2014;Cheng et al, 2009;Oppenheimer et al, 2004), leading to human health impact (Chiusolo et al, 2011;Crouse et al, 2015;He et al, 2020a;Hoek et al, 2013;Pongpiachan et al, 2018;Pongpiachan and Paowa, 2015;Ren-Jian et al, 2012). NO 2 has been considered worldwide as an important indicator of environmental pollution and is listed as one of six typical air pollutants by…”

Effects of day-of-week trends and vehicle types on PM2.5-bounded carbonaceous compositions

“…During the past few years, autocorrelation plots have been widely employed in several atmospheric environmental studies, including an investigation of the impact of meteorological parameters and trace gas concentrations on daily hospital walk-ins and admissions in ChiangMai, Thailand (Pongpiachan and Paowa, 2014), long-term observations and modelling of aerosol loading over the Indo-Gangetic plains (IGP), India (Soni et al, 2014), and pedestrian exposure to PM 2.5 in Sydney, Australia (Greaves et al, 2008). Because autocorrelation represents the similarity between observations as a function of the time lag between them, it appears reasonable to evaluate the randomness of carbonaceous compositions with time using this mathematical tool.…”