What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas?

The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Peaks, sources, and immediate health impacts of PM2.5 and PM1 exposure in Indonesia and Taiwan with microsensors.

BACKGROUND: Microsensors have been used for the high-resolution particulate matter (PM) monitoring. OBJECTIVES: This study applies PM and health microsensors with the objective of assessing the peak exposure, sources, and immediate health impacts of PM2.5 and PM1 in two Asian countries. METHODS: Exposure assessment and health evaluation were carried out for 50 subjects in 2018 and 2019 in Bandung, Indonesia and for 55 subjects in 2019 and 2020 in Kaohsiung, Taiwan. Calibrated AS-LUNG sets and medical-certified RootiRx® sensors were used to assess PM and heart-rate variability (HRV), respectively. RESULTS: Overall, the 5-min mean exposure of PM2.5 and PM1 was 30.4 ± 20.0 and 27.0 ± 15.7 µg/m3 in Indonesia and 14.9 ± 11.2 and 13.9 ± 9.8 µg/m3 in Taiwan, respectively. The maximum 5-min peak PM2.5 and PM1 exposures were 473.6 and 154.0 µg/m3 in Indonesia and 467.4 and 217.7 µg/m3 in Taiwan, respectively. Community factories and mosquito coil burning are the two most important exposure sources, resulting in, on average, 4.73 and 5.82 µg/m3 higher PM2.5 exposure increments for Indonesian subjects and 10.1 and 9.82 µg/m3 higher PM2.5 exposure for Taiwanese subjects compared to non-exposure periods, respectively. Moreover, agricultural waste burning and incense burning were another two important exposure sources, but only in Taiwan. Furthermore, 5-min PM2.5 and PM1 exposure had statistically significantly immediate impacts on the HRV indices and heart rates of all subjects in Taiwan and the scooter subjects in Indonesia with generalized additive mixed models. The HRV change for a 10 µg/m3 increase in PM2.5 and PM1 ranged from -0.9% to -2.5% except for ratio of low-high frequency, with greater impacts associated with PM1 than PM2.5 in both countries. IMPACT STATEMENT: This work highlights the ability of microsensors to capture high peaks of PM2.5 and PM1, to identify exposure sources through the integration of activity records, and to assess immediate changes in heart rate variability for a panel of approximately 50 subjects in Indonesia and Taiwan. This study stands out as one of the few to demonstrate the immediate health impacts of peak PM, complementing to the short-term (days or weeks) or long-term effects (months or longer) assessed in most epidemiological studies. The technology/methodology employed offer great potential for researchers in the resource-limited countries with high PM2.5 and PM1 levels.

In-depth analysis of ambient air pollution changes due to the COVID-19 pandemic in the Asian Monsoon region.

The COVID-19 pandemic has given a chance for researchers and policymakers all over the world to study the impact of lockdowns on air quality in each country. This review aims to investigate the impact of the restriction of activities during the lockdowns in the Asian Monsoon region on the main criteria air pollutants. The various types of lockdowns implemented in each country were based on the severity of the COVID-19 pandemic. The concentrations of major air pollutants, especially particulate matter (PM) and nitrogen dioxide (NO2), reduced significantly in all countries, especially in South Asia (India and Bangladesh), during periods of full lockdown. There were also indications of a significant reduction of sulfur dioxide (SO2) and carbon monoxide (CO). At the same time, there were indications of increasing trends in surface ozone (O3), presumably due to nonlinear chemistry associated with the reduction of oxides of nitrogens (NOX). The reduction in the concentration of air pollutants can also be seen in satellite images. The results of aerosol optical depth (AOD) values followed the PM concentrations in many cities. A significant reduction of NO2 was recorded by satellite images in almost all cities in the Asian Monsoon region. The major reductions in air pollutants were associated with reductions in mobility. Pakistan, Bangladesh, Myanmar, Vietnam, and Taiwan had comparatively positive gross domestic product growth indices in comparison to other Asian Monsoon nations during the COVID-19 pandemic. A positive outcome suggests that the economy of these nations, particularly in terms of industrial activity, persisted during the COVID-19 pandemic. Overall, the lockdowns implemented during COVID-19 suggest that air quality in the Asian Monsoon region can be improved by the reduction of emissions, especially those due to mobility as an indicator of traffic in major cities.

Exploring interconnections: A comprehensive multi-country analysis of climate change, energy demand, long-term care, and health of older adults.

Challenges faced by many countries are energy insecurity, climate change, and the health and long-term care of growing numbers of older people. These challenges are increasingly intersecting with rising energy prices, aging populations, and an increased frequency and intensity of extreme climate events. This paper gives a deeper understanding of the current and predicted interconnections among these challenges through narrative-driven content and thematic analysis from workshops with a diverse group of international stakeholders from the Global North and Global South. Narratives emerged highlighting a complex nexus of interconnections and presenting critical action areas. Targeted local and global policies and interventions are needed to alleviate stress on health systems, encourage the integrated uptake of clean energy sources, and uphold social justice across all economies. Professionals can use this work to inform the design and implementation of effective interventions and increase the resilience of older adults by better preparing for systemic risks.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Evaluating long-term and high spatiotemporal resolution of wet-bulb globe temperature through land-use based machine learning model.

BACKGROUND: The increase in global temperature and urban warming has led to the exacerbation of heatwaves, which negatively affect human health and cause long-term loss of work productivity. Therefore, a global assessment in temperature variation is essential. OBJECTIVE: This paper is the first of its kind to propose land-use based spatial machine learning (LBSM) models for predicting highly spatial-temporal variations of wet-bulb globe temperature (WBGT), which is a heat stress indicator used to assess thermal comfort in indoor and outdoor environments, specifically for the main island of Taiwan. METHODS: To develop spatiotemporal prediction models for both the working period and noon period, we calculated the WBGT of each weather station from 2001 to 2019 using temperature, humidity, and solar radiation data. These WBGT estimations were then used as the dependent variable for developing the spatiotemporal prediction models. To enhance model performance, we used innovative approaches that combined SHapley Additive exPlanations (SHAP) values for the selection of non-linear variables, along with machine learning algorithms for model development. RESULTS: When incorporating temperature along with other land-use/land cover predictor variables, the performance of LBSM models was excellent, with an R2 value of up to 0.99. The LBSM models explained 98% and 99% of the spatial-temporal variations in WBGT for the working and noon periods, respectively, within the complete models. In the temperature-excluded models, the explained variances were 94% and 96% for the working and noon periods, respectively. IMPACT: WBGT is a common method used by many organizations to access the impact of heat stress on human beings. However, limited studies have mentioned the association between WBGT and health impacts due to the absence of spatiotemporal databases. This study develops a new approach using land-use-based spatial machine learning (LBSM) models to better predict the fine spatial-temporal WBGT levels, with a 50-m × 50-m grid resolution for both working time and noontime. Our proposed methodology could be used in future studies aimed at evaluating the potential long-term loss of work productivity due to the effects of global warming or urban heat island.

A pilot heat-health warning system co-designed for a subtropical city.

Significant heat-related casualties underlie the urgency of establishing a heat-health warning system (HHWS). This paper presents an evidence-based pilot HHWS developed for Taipei City, Taiwan, through a co-design process engaging stakeholders. In the co-design process, policy concerns related to biometeorology, epidemiology and public health, and risk communication aspects were identified, with knowledge gaps being filled by subsequent findings. The biometeorological results revealed that Taipei residents were exposed to wet-bulb globe temperature (WBGT) levels of health concern for at least 100 days in 2016. The hot spots and periods identified using WBGT would be missed out if using temperature, underlining the importance of adopting an appropriate heat indicator. Significant increases in heat-related emergency were found in Taipei at WBGT exceeding 36°C with reference-adjusted risk ratio (RaRR) of 2.42, taking 30°C as the reference; and residents aged 0-14 had the highest risk enhancement (RaRR = 7.70). As for risk communication, occurring frequency was evaluated to avoid too frequent warnings, which would numb the public and exhaust resources. After integrating knowledge and reconciling the different preferences and perspectives, the pilot HHWS was co-implemented in 2018 by the science team and Taipei City officials; accompanying responsive measures were formulated for execution by ten city government departments/offices. The results of this pilot served as a useful reference for establishing a nationwide heat-alert app in 2021/2022. The lessons learnt during the interactive co-design processes provide valuable insights for establishing HHWSs worldwide.

Greenspace related to bipolar disorder in Taiwan: Quantitative benefits of saving DALY loss and increasing income.

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.e., disability-adjusted life year loss and income) were used to represent the benefits. The incidence rate of bipolar disorder was obtained from Taiwan's National Health Insurance Research Database. Normalized different vegetation index (NDVI) was measured as a proxy for the greenspace availability. A generalized additive mixed model coupled with a sensitivity test were applied to evaluate the statistical association. The prevented fraction for the population (PFP) was then applied to develop a scenario for quantifying benefit. The result showed a significant negative association between greenspace and bipolar disorder in Taiwan. Compared to low greenspace, areas with medium and high greenspace may reduce the bipolar risk by 21% (RR = 0.79; 95% CI = 0.76-0.83) and 51% (RR = 0.49; 95% CI = 0.45-0.53). Calculating benefits, we found that the development of a scenario by increasing greenspace adjusted for availability indicator in township categorized as low greenspace could save in DALY loss due to bipolar disorder up to10.97% and increase in income up to 11.04% from the current situation. Lastly, this was the first study in Asia-Pacific to apply a customized greenspace increment scenario to quantify the benefits to a particular health burden such as bipolar disorder.

Impact of physical and social living environments on pro-environmental intentions.

The living environment might play an important role in shaping the pro-environmental intentions of the people. However, there was limited research on how the living environments influenced the pro-environmental intentions of people. The objectives of this study are to evaluate the direct effects of physical and social environments on pro-environmental intentions as well as the mediating effects of environmental attitudes and life satisfaction. Structural Equation Modeling was used with data extracted from the 2020 Taiwan Social Change Survey database (n = 1671). Results showed direct positive associations of both physical and social environments with pro-environmental intentions (ß = 0.133 and ß = 0.076, respectively) as well as indirect positive associations via the life satisfaction-mediating pathway (ß = 0.031 and ß = 0.044, respectively). The physical environment negatively influenced pro-environmental intentions through the environmental attitude pathway (ß = - 0.255) with unpleasant neighborhood enhancing the pro-environmental intentions of residents. Taken together, the overall effect of the physical environment was negative (ß = - 0.093) while that of the social environment was positive (ß = 0.109). The most important factors for the physical and social environments were disturbance and livability in north, central and south Taiwan, neighborhood pollution and interestingness in east Taiwan. Accordingly, minimizing disturbance and neighborhood pollution of the physical environment could have the highest effect on pro-environmental intentions enhancement in western and eastern Taiwan, respectively. For the social environment, improving livability in the west and interestingness in the east would have an even larger impact on pro-environmental intentions. This study emphasized the importance of neighborhood environment on the environmental intentions of the people. The study also identified the important factors for policymakers to target to achieve the best effect on improving environmental intentions.

Effects of indoor air quality and home environmental characteristics on allergic diseases among preschool children in the Greater Taipei Area.

Indoor air quality and home environmental characteristics are potential factors associated with the onset and exacerbation of allergic diseases. Our study examined the effects of these factors on allergic diseases (i.e., asthma, allergic rhinitis, allergic conjunctivitis, and atopic dermatitis) among preschool children. We recruited a total of 120 preschool children from an ongoing birth cohort study in the Greater Taipei Area. A comprehensive environmental evaluation was conducted at each participant's residence and included measurements of indoor and outdoor air pollutants, fungal spores, endotoxins, and house dust mite allergens. A structured questionnaire was used to collect information on the allergic diseases and home environments of participants. Land-use characteristics and points of interest in the surrounding area of each home were analyzed. Other covariates were obtained from the cohort data. Multiple logistic regressions were used to examine the relationships between allergic diseases and covariates. We observed that all mean indoor air pollutant levels were below Taiwan's indoor air quality standards. After adjustment for covariates, the total number of fungal spores and the ozone, Der f 1, and endotoxin levels were significantly associated with increased risks of allergic diseases. Biological contaminants more significantly affected allergic diseases than other pollutants. Moreover, home environmental characteristics (e.g., living near power facilities and gas stations) were associated with an increased risk of allergic diseases. Regular and proper home sanitation is recommended to prevent the accumulation of indoor pollutants, especially biological contaminants. Living away from potential sources of pollution is also crucial for protecting the health of children.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan.

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan.

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM2.5 concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM2.5 in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM2.5 exposures. In terms of air pollution spatial modelling, using only a single model might lose information in capturing spatial and temporal correlation between predictors and pollutant levels. Hence, this study aimed to propose an ensemble mixed spatial model that incorporated Kriging interpolation, land-use regression (LUR), machine learning, and stacking ensemble approach to estimate long-term PM2.5 variations for nearly three decades in daytime and nighttime. Three steps of model development were applied: 1) linear based LUR and Hybrid Kriging-LUR were used to determine influential predictors; 2) machine learning algorithms were used to enhance model prediction accuracy; 3) predictions from the selected machine learning models were fitted and evaluated again to build the final ensemble mixed spatial model. The results showed that prediction performance increased from 0.514 to 0.895 for daily, 0.478 to 0.879 for daytime, and 0.523 to 0.878 for nighttime when applying the proposed ensemble mixed spatial model compared with LUR. Results of overfitting test and extrapolation ability test confirmed the robustness and reliability of the developed models. The distance to the nearest thermal power plant, density of soil and pebbles fields, and funeral facilities might affect the variation of PM2.5 levels between daytime and nighttime. The PM2.5 level was higher in daytime compared with nighttime with little difference, revealing the importance of estimating nighttime PM2.5 variations. Our findings also clarified the emission sources in daytime and nighttime, which serve as valuable information for air pollution control strategies establishment.

Model development and validation of personal exposure to PM2.5 among urban elders.

Indirect measurements through a combination of microenvironment concentrations and personal activity diaries provide a potentially useful alternative for PM2.5 exposure estimates. This study was to optimize a personal exposure model based on spatiotemporal model predictions for PM2.5 exposure in a sub-cohort study. Personal, home indoor, home outdoor, and ambient monitoring data of PM2.5 were conducted for an elderly population in the Taipei city of Taiwan. The proposed microenvironment exposure (ME) models incorporate PM2.5 measurements and individual time-activity information with a generalized estimating equation (GEE) analysis. We evaluated model performance with daily personal PM2.5 exposure based on the coefficient of determination, accuracy, and mean bias error. Ambient and home outdoor measures as exposure surrogates are likely to under- and overestimate personal exposure to PM2.5 in our study population, respectively. Measured and predicted indoor exposures were highly correlated with personal PM2.5 exposure. The awareness of peculiar smells is an important factor that significantly increases personal PM2.5 exposure by 46-70%. The model incorporating home indoor PM2.5 can achieve the highest agreement (R2 = 0.790) with personal exposure and the lowest measurement error. The ME model with the GEE analysis combining home outdoor PM2.5 determined by LUR model with a machine learning technique can improve the prediction (R2 = 0.592) of personal PM2.5 exposure, compared with the prediction of the traditional LUR model (R2 = 0.385).

Sex-differences in the effects of indoor air pollutants and household environment on preschool child cognitive development.

Air pollution, outdoor residential environment, indoor household characteristics, and parental mental health are potential factors associated with child development. However, few studies have simultaneously analyzed the association between the aforementioned factors and preschool child (aged 2-5 years) development. This study investigated the effects of those factors on child development and their potential modifying effects. A total of 142 participants were recruited from a birth cohort study in the Greater Taipei Area, and the evaluation was conducted at each participant's home from 2017 to 2020. Child cognitive development was assessed by psychologists using the Bayley Scales of Infant and Toddler Development and the Wechsler Preschool & Primary Scale of Intelligence. Household air pollutants, outdoor residential environment, indoor household characteristics, parental mental health, and other covariates were evaluated. Multiple regressions were used to examine the relationships between child development and covariates. Stratified analysis by child sex and parental mental health was conducted. Average indoor air pollutant levels were below Taiwan's Indoor Air Quality Standards. After adjustment for covariates, the indoor total volatile organic compounds (TVOCs) level was significantly associated with poor child development (per interquartile range increase in the TVOC level was associated with a 5.1 percentile decrease in child cognitive development). Sex difference was observed for the association between TVOC exposure and child development. Living near schools, burning incense at home, purchasing new furniture, and parental anxiety were related to child development. Indoor TVOC level was associated with poor child cognitive development, specifically with the girls. Indoor and outdoor residential environment and parental anxiety interfered with child development. TVOCs should be used cautiously at home to minimize child exposure. A low-pollution living environment should be provided to ensure children's healthy development.

Northern Hemisphere Urban Heat Stress and Associated Labor Hour Hazard from ERA5 Reanalysis.

Increasing surface air temperature is a fundamental characteristic of a warming world. Rising temperatures have potential impacts on human health through heat stress. One heat stress metric is the wet-bulb globe temperature, which takes into consideration the effects of radiation, humidity, and wind speed. It also has broad health and environmental implications. This study presents wet-bulb globe temperatures calculated from the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis and combines it with health guidelines to assess heat stress variability and the potential for reduction in labor hours over the past decade on both the continental and urban scale. Compared to 2010-2014, there was a general increase in heat stress during the period from 2015 to 2019 throughout the northern hemisphere, with the largest warming found in tropical regions, especially in the northern part of the Indian Peninsula. On the urban scale, our results suggest that heat stress might have led to a reduction in labor hours by up to ~20% in some Asian cities subject to work-rest regulations. Extremes in heat stress can be explained by changes in radiation and circulation. The resultant threat is highest in developing countries in tropical areas where workers often have limited legal protection and healthcare. The effect of heat stress exposure is therefore a collective challenge with environmental, economic, and social implications.

Exploring the Potential Relationship Between Global Greenness and DALY Loss Due to Depressive Disorders.

Objective: Prior studies have shown that greenness can reduce the burden of depressive disorders. However, most were focused on local-scale analyses while limited evaluated globally. We aimed to investigate the association between greenness and the burden of depressive disorders using data from 183 countries worldwide. Methods: We used the normalized difference vegetation index (NDVI) to estimate greenness. Country-level disability-adjusted life year (DALY) loss due to depressive disorders was used to represent depressive disorder burdens. A generalized linear mixed model was applied to assess the relationship between greenness and depressive disorders after controlling for covariates. Stratified analyses were conducted to determine the effects of greenness across several socio-demographic levels. Results: The findings showed a significant negative association between greenness and the health burden of depressive disorders with a coefficient of -0.196 (95% CI: -0.356, -0.035) in the DALY changes per interquartile unit increment of NDVI. The stratified analyses suggested beneficial effects of greenness on depressive disorders across sex, various age groups especially for those aged <49 years, with low-income and/or those living in highly urbanized countries. Conclusions: Our study noted that greenness exposure was significant negative association with the burden of depressive disorders. The findings should be viewed as recommendations for relevant authorities in supporting environmental greenness enhancement to reduce the mental burdens.

Particulate matters, aldehydes, and polycyclic aromatic hydrocarbons produced from deep-frying emissions: comparisons of three cooking oils with distinct fatty acid profiles.

It is recognized that hazardous emissions produced from frying oils may be related to oil properties, particularly the fatty acid composition. However, investigations have been limited and partial. In this work, the emissions from deep-frying foods with three oils (palm, olive, and soybean oils) with distinct fatty acid profiles were comprehensively examined in a simulated kitchen, and the interrelationship among emitted substances, oil quality parameters, and fatty acids profiles was explored. Firstly, palm oil emitted the highest number concentration of total particle matters ((3895 ± 1796) × 103 #/cm3), mainly in the Aitken mode (20-100 nm). We observed a positive correlation between particle number concentration and levels of palmitic acid, a major saturated fatty acid (SAFA) (rs = 0.73, p < 0.05), and total polar compounds (TPC) (rs = 0.68, p < 0.05) in the fried oil, a degradation marker which was also positively correlated with that of black carbon (BC) (rs = 0.68, p < 0.05). Secondly, soybean oil emitted the highest level of gaseous aldehydes (3636 ± 607 µg/m3), including acrolein, propinoaldehyde, crotonaldehyde, hexanal, and trans-2-heptenal; the total aldehyde concentration were positively correlated with α-linolenic acid (ALA) percentage (rs = 0.78, p < 0.01), while hexanal and trans-2-heptenal were with linoleic acid (LA) (rs = 0.73 and 0.67, p < 0.05). LA and ALA were two major polyunsaturated fatty acids in non-tropical plant oils. Thirdly, palm oil emitted the most particle-bound polycyclic aromatic hydrocarbons (PAHs), and a positive association was discovered between two PAHs and SAFA percentage. Olive oil seems superior to soybean and palm oils with regards to toxic emissions during deep-frying.

Spatial-temporal variability and heath impact of particulate matter during a 2019-2020 biomass burning event in Southeast Asia.

To understand the characteristics of particulate matter (PM) in the Southeast Asia region, the spatial-temporal concentrations of PM10, PM2.5 and PM1 in Malaysia (Putrajaya, Bukit Fraser and Kota Samarahan) and Thailand (Chiang Mai) were determined using the AS-LUNG V.2 Outdoor sensor. The period of measurement was over a year from 2019 to 2020. The highest concentrations of all sizes of PM in Putrajaya, Bukit Fraser and Kota Samarahan were observed in September 2019 while the highest PM10, PM2.5 and PM1 concentrations in Chiang Mai were observed between March and early April 2020 with 24 h average concentrations during haze days in ranges 83.7-216 µg m-3, 78.3-209 µg m-3 and 57.2-140 µg m-3, respectively. The average PM2.5/PM10 ratio during haze days was 0.93 ± 0.05, which was higher than the average for normal days (0.89 ± 0.13) for all sites, indicating higher PM2.5 concentrations during haze days compared to normal days. An analysis of particle deposition in the human respiratory tract showed a higher total deposition fraction value during haze days than on non-haze days. The result from this study indicated that Malaysia and Thailand are highly affected by biomass burning activity during the dry seasons and the Southwest monsoon.

Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries.

The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project-Monsoon Asia and Oceania Networking Group (IGAC-MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure-health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed.