Land cover, land use changes and air pollution in Asia: a synthesis

Weather impact on ambient air pollution and its association with land use types/activities over 5,572 municipalities in Brazil

Quantify the impact of meteorological changes on air pollution levels is the aim of numerous recent studies. However, there is still a lack of investigations assessing the influence of land use/activities on the relationship between climate and air quality. In this study, we used a two-stage design to estimate the influence of land use types and activities on the association between weather changes and air pollution (PM2.5, NO2, SO2, O3) over 5572 municipalities in Brazil. To calculate the influence of recent weather change on air pollution concentration for each municipality, we used the "weather penalty" concept. This approach considers differences in linear trend coefficients between two generalized additive models. Then, using quantile regression, we estimated the effect of land use types and activities (8 variables related to transportation, energy generation, and land use) on weather-related increases in ambient air pollution. We found that an increase in PM2.5 was associated to recent weather changes in most municipalities (average increase of 0.07μg/m3per year) and a decrease in NO2 in most municipalities (average decrease of 0.0003 ppb per year). O3 and SO2 had more intense increases associated with weather changes in the North region. Our findings suggest the most robust positive associations between weather penalties on PM2.5 and areas with non-clean energy and oil refineries (average increase of 0.006μg/m3per year and 0.04μg/m3per year, respectively). We also found positive associations between Pasture areas, urban areas, and transportation and the weather penalties of this pollutant. In contrast, forest areas were negatively associated with PM2.5 penalties. We also found that oil refineries, urban areas, and transportation significantly positively influenced weather penalties for SO2 and O3. Overall, the study highlights the importance of considering the influence of land use types and activities on weather-related changes in ambient air pollution.

Land surface temperature and transboundary air pollution: a case of Bangkok Metropolitan Region

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

Source emission contributions to particulate matter and ozone, and their health impacts in Southeast Asia

Southeast Asia has been experiencing severe air pollution due to its substantial local emissions and transboundary air pollution (TAP), causing significant health impacts. While literature focused on air pollution episodes in Southeast Asia, we have yet to fully understand the contributions of local emission sectors and TAP to air quality in the region annually. Herein we employed air quality modeling with the species tagging method to first assess the contributions of source sectors and locations to fine particulate matter (PM2.5) and ozone (O3) in Southeast Asia and to hence quantify the resultant health impacts. Our results show that air pollutant exposure was associated with ∼ 900 thousand premature mortalities in Southeast Asia every year. Of which, 77 % and 23 % were due to local emissions and TAP in the region, respectively. ∼ 87 % of the premature mortalities due to local emissions were induced by PM2.5 exposure, whereas the remaining were due to O3 exposure. PM2.5-related health impacts were dominated by industrial (45 %) and residential (17 %) emissions, and O3-related impacts were mainly due to biogenic (40 %) and road transport (24 %) emissions. Furthermore, the health impacts of TAP were particularly adverse in Brunei, East Timor, Singapore, Laos, and border regions.

Is air pollution detrimental to regional innovation? An empirical heterogeneity test based on Chinese cities

Nowadays, innovation seems to be the inevitable choice to achieve stable economic growth. However, the negative impact of air pollution on health and economy makes air pollution an important factor in regional innovation, which deserves our discussion. The overall regional innovation level from 2014 to 2019 has an upward trend, while the overall air pollution has a downward trend during the period, which provides foundation for our research. Based on the data of 285 prefecture-level cities in China from 2014 to 2019, this paper uses the fixed effect and mediation model to verify the impact and mechanism of air pollution on regional innovation. The results show that the increase in air pollution, measured by the air quality index, significantly inhibits regional innovation. Air pollution has significant funds crowding-out effect and human capital loss effect, thereby decreasing the regional innovation level, which means innovation funds and researchers play a conductive role between air pollution and regional innovation. In heterogeneity analysis, it is found that the detrimental effect of air pollution on regional innovation is significant in eastern and central China, in large- and medium-sized cities, and in cities with poor or general air quality. It indicates that developed and large-scale regions should pay more attention to air pollution control. For polluted regions, more emphasis and endeavors are needed to address air pollution problems. Besides, the inhibitory effect is more severe on incremental innovation rather than on radical innovation, which deserves the attention of enterprises engaged in incremental innovation. Therefore, we propose that targeted environmental policies and effective measures should be developed to improve air quality in the long run. Moreover, policymakers could provide strong support for innovation grants, talent subsidies, and rewards and encourage clean technological innovation through short-term trade-offs between heavily polluting and low polluting enterprises.

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM2.5 and PM10) Concentrations

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e., analysis of covariance (ANCOVA) and random forest regression (RFR)) for estimating daily PM_2.5 and PM₁₀ concentrations in Brunei Darussalam. These models were first derived from past AQ and meteorological measurements in Singapore and then tested with AQ and meteorological data from Brunei Darussalam. The results show that the ANCOVA model (R2 = 0.94 and RMSE = 0.05 µg/m³ for PM_2.5, and R2 = 0.72 and RMSE = 0.09 µg/m³ for PM₁₀) could describe daily PM concentrations over 18 µg/m³ in Brunei Darussalam much better than the RFR model (R2 = 0.92 and RMSE = 0.04 µg/m³ for PM_2.5, and R2 = 0.86 and RMSE = 0.08 µg/m³ for PM₁₀). In conclusion, the derived models provide a satisfactory estimation of PM concentrations for both countries despite some limitations. This study shows the potential of the models for inter-country PM estimations in Southeast Asia.

Biomass burning-derived airborne particulate matter in Southeast Asia: A critical review

Smoke haze episodes, resulting from uncontrolled biomass burning (BB) including forest and peat fires, continue to occur in Southeast Asia (SEA), affecting air quality, atmospheric visibility, climate, ecosystems, hydrologic cycle and human health. The pollutant of major concern in smoke haze is airborne particulate matter (PM). A number of fundamental laboratory, field and modeling studies have been conducted in SEA from 2010 to 2020 to investigate potential environmental and health impacts of BB-induced PM. The goal of this review is to bring together the most recent developments in our understanding of various aspects of BB-derived PM based on 127 research articles published from 2010 to 2020, which have not been conveyed in previous reviews. Specifically, this paper discusses the physical, chemical, toxicological and radiative properties of BB-derived PM. It also provides insights into the environmental and health impacts of BB-derived PM, summarizes the approaches taken to do the source apportionment of PM during BB events and discusses the mitigation of exposure to BB-derived PM. Suggestions for future research priorities are outlined. Policies needed to prevent future BB events in the SEA region are highlighted.

Global estimation of mortality, disability-adjusted life years and welfare cost from exposure to ambient air pollution

Environmental pollution in the era of sustained economic development is an inevitable occurrence. However, the rising levels of pollutant emissions hamper air quality, hence, affecting health outcomes. Previous studies have assessed the case-by-case effect of ambient air pollution on mortality and morbidity, however, the impact on disability-adjusted life years (DALYs) and welfare cost have not been investigated entirely. Here, we conduct an empirical analysis of the 28-Year trend to analyze the nexus between ambient particulate matter and ozone, mortality, DALYs, and welfare cost across 195 countries and territories by employing novel dynamic panel estimation methods. We find that none of the 195 countries and territories studied between 1990 and 2017 meet WHO guideline for air quality, thus, mitigating ambient air pollution is at risk. However, Spain with an annual average of PM_2.5 not exceeding 15.12 μg/m³ is closer to WHO guideline of 10 μg/m³/annum. Among the countries (China, the US, Russia, India, Germany and Japan) with the highest welfare cost of premature death associated with the exposure to outdoor PM_2.5 and ozone, China is the most vulnerable to economic burden - spending US$1.58 trillion (constant 2010) in 2017. This study demonstrates that ambient air pollution has a significant impact on economic development (welfare cost) and health outcomes (mortality, premature deaths, and DALYs).

Preparing Adolescents for the Uncertain Future: Concepts, Tools and Strategies for Teaching Anthropogenic Environmental Change

Humankind is increasingly being challenged by anthropogenic environmental changes and society needs to be better equipped with knowledge, skills and values to adapt to these changes. This poses new challenges for school education. We propose a framework towards future-oriented education by addressing three issues: a) How can the school curriculum be reframed to take account of anthropogenic environmental changes? b) What difficulties do students encounter when learning about these changes? c) What learning tools and pedagogical strategies are best suited to effectively and efficiently teach about environmental changes? An example is provided, whereby secondary school students engage with the topic of deforestation using geospatial technology. This study informs curriculum makers and instructors in providing education that enhances adolescents’ understanding of the uncertain world and increases their ability to be proactive, rather than merely responding to change.

Source Apportionment of Coarse Particulate Matter (PM10) in Yangon, Myanmar

The Republic of the Union of Myanmar is one of many developing countries facing concerns about particulate matter (PM). Previously, a preliminary study of PM_2.5 in 2018 suggested that the main source of PM in Yangon, the former capital, was vehicle emissions. However, this suggestion was not supported by any chemical composition data. In this study, to fill that gap, we quantitatively determined source contributions to coarse particulate matter (PM₁₀) in Yangon, Myanmar. PM₁₀ samples were collected in Yangon from May 2017 to April 2018 and chemically analyzed to determine composition. Chemical composition data for these samples were then used in the Chemical Mass Balance (CMB) model to identify the major sources of particulate matter in this area. The results indicate that PM₁₀ composition varies seasonally according to both meteorological factors (e.g., precipitation and temperature) and human activities (e.g., firewood and yard waste burning). The major sources of PM in Yangon annually were dust, secondary inorganic aerosols (SIA), and secondary organic aerosols (SOA), while contributions from biomass burning were more important during the winter months.

Forest and Land Fires Are Mainly Associated with Deforestation in Riau Province, Indonesia

Indonesia has experienced extensive land-cover change and frequent vegetation and land fires in the past few decades. We combined a new land-cover dataset with satellite data on the timing and location of fires to make the first detailed assessment of the association of fire with specific land-cover transitions in Riau, Sumatra. During 1990 to 2017, secondary peat swamp forest declined in area from 40,000 to 10,000 km2 and plantations (including oil palm) increased from around 10,000 to 40,000 km2. The dominant land use transitions were secondary peat swamp forest converting directly to plantation, or first to shrub and then to plantation. During 2001–2017, we find that the frequency of fire is greatest in regions that change land-cover, with the greatest frequency in regions that transition from secondary peat swamp forest to shrub or plantation (0.15 km−2 yr−1). Areas that did not change land cover exhibit lower fire frequency, with shrub (0.06 km−2 yr−1) exhibiting a frequency of fire >60 times the frequency of fire in primary forest. Our analysis demonstrates that in Riau, fire is closely connected to land-cover change, and that the majority of fire is associated with the transition of secondary forest to shrub and plantation. Reducing the frequency of fire in Riau will require enhanced protection of secondary forests and restoration of shrub to natural forest.

Removal of labile arsenic from flooded paddy soils with a novel extractive column loaded with quartz-supported nanoscale zero-valent iron

Efficient removal of labile arsenic (As) from paddy soil is a fundamental pathway mitigating As accumulation in rice from a long-term perspective. In this study, a porous and pencil-shaped column prepacked with quartz-supported nanoscale zero-valent iron (NZVI) was designed to extract elevated porewater As from paddy soil under flooded condition. With fine quartz as supporting medium in the core layer, only 0.07% out-migration of the loaded NZVI occurred in arsenite As(III) solution. At pH 5-9, removal of aqueous As(III) with NZVI-column was 73-78%, while silicic acid and phosphate at their environmentally realistic concentrations exhibited 27-30% and 14-17% inhibition on As(III) extraction, respectively. For two paddy soils with slight (S-As) and moderate (M-As) As contamination, four cycles of intermittent extraction with NZVI-column induced steady and marked decrease in porewater As. By the end of four successive extractions, profiles of DGT-labile As in S-As and M-As soils decreased by 22% and 29% on average with simultaneous decline of the most available fraction of soil As (including soluble and exchangeable fraction) by 26% and 17%, respectively. For the post-extracted two soils, As accumulation of rice seedlings declined by 29-57% than those in control. These results identify the effectiveness of NZVI-column in extracting elevated labile As from paddy soils with the aid of flooding. Targeting fast removal of high porewater As, column-extraction could serve as the first step in "remediation train" of paddy soils with relatively high As to shorten cleanup time by rendering much lowered soil As burden for the following phytoextraction and other measures.

Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin

The temporal distributions of meteorological drivers and air pollutants over Dibrugarh, a location in the upper Brahmaputra basin, are studied using observations, models and reanalysis data. The study aims to assess the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the WRF coupled with Sulfur Transport dEposition Model (WRF-STEM), and Copernicus Atmosphere Monitoring Service (CAMS) model over Dibrugarh for the first time. The meteorological variables and air pollutants viz., black carbon(BC), carbon monoxide(CO), sulphur dioxide(SO2), Ozone(O3), and oxides of Nitrogen(NOx) obtained from WRF-Chem, WRF-STEM and CAMS are evaluated with observations. The source region tagged CO simulated by WRF-STEM delineate the regional contribution of CO. The principal source region of anthropogenic CO over Dibrugarh is North-Eastern India with a 59% contribution followed by that from China (17%), Indo-Gangetic Plains (14%), Bangladesh (6%), other parts of India (3%) and other regions (1%). Further, the BC-CO regression analysis is used to delineate the local emission sources. The BC-CO correlations estimated from models (0.99 for WRF-Chem, 0.96 for WRF-STEM, 0.89 for CAMS), and reanalysis (0.8 for Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) are maximum in pre-monsoon whereas surface observations show highest correlations (0.81) in winter. In pre-monsoon season, 90% of the modeled CO is due to biomass burning over Dibrugarh.

Trends in Vegetation fires in South and Southeast Asian Countries

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003–2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012–2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.