Exploration of the Contribution of Fire Carbon Emissions to PM2.5 and Their Influencing Factors in Laotian Tropical Rainforests

Abstract

It is of great significance to understand the drivers of PM2.5 and fire carbon emission (FCE) and the relationship between them for the prevention, control, and policy formulation of severe PM2.5 exposure in areas where biomass burning is a major source. In this study, we considered northern Laos as the area of research, and we utilized space cluster analysis to present the spatial pattern of PM2.5 and FCE from 2003–2019. With the use of a random forest and structural equation model, we explored the relationship between PM2.5 and FCE and their drivers. The key results during the target period of the study were as follows: (1) the HH (high/high) clusters of PM2.5 concentration and FCE were very similar and distributed in the west of the study area; (2) compared with the contribution of climate variables, the contribution of FCE to PM2.5 was weak but statistically significant. The standardized coefficients were 0.5 for drought index, 0.32 for diurnal temperature range, and 0.22 for FCE; (3) climate factors are the main drivers of PM2.5 and FCE in northern Laos, among which drought and diurnal temperature range are the most influential factors. We believe that, as the heat intensifies driven by climate in tropical rainforests, this exploration and discovery can help regulators and researchers better integrate drought and diurnal temperature range into FCE and PM2.5 predictive models in order to develop effective measures to prevent and control air pollution in areas affected by biomass combustion.

Modeling the Effects of Drivers on PM2.5 in the Yangtze River Delta with Geographically Weighted Random Forest

“…The resulting PM 2.5 data is a global dataset that provides information on the atmosphere's concentration and distribution of fine particulate matter. This data has been widely used to monitor air quality [7], track the movement of pollutants [20], and study the health effects of exposure to PM 2.5 [11], and it can be accessed from the Atmospheric Composition Analysis Group (ACAG) at the University of Washington [3]. Van Donkelaar et al [7] confirmed that this data can be easily applied to China.…”

“…Therefore, we used the monthly mean temperature, diurnal temperature range, precipitation, and potential evapotranspiration data during 2003-2020 provided by the Climatic Research Unit gridded Time Series (CRU TS) as climate-independent variables in this study. CRU TS is a well-recognized meteorological dataset produced by the National Center for Atmospheric Sciences (NCAS) in the United Kingdom [20,36]. The data acquisition address is available in Table 1.…”

“…[12,44,45] Extreme weather events and severe droughts have recently increased the frequency of wildfires worldwide, leading to more subsequent carbon emissions from fires [35]. According to several recent studies, it also has some surprising effects on notorious air pollution [20,34]. Our research employed the aridity index (AI), the ratio of annual mean precipitation (P) to potential evapotranspiration (PET), as a measure of drought [20].…”

“…Nevertheless, the complex and nonlinear relationships between PM 2.5 and environmental drivers have not been fully discussed. An effective way to solve this problem is with a ML algorithm, including different artificial neural networks [4,[17][18][19], support vector machines [16], decision trees [5], gradient boosting machines [1,15,18], and random forests (RF) [9,12,20]. In addition to their ability to capture nonlinear relationships, ML techniques are good at environmental sciences data analysis and modeling because of their abilities to compute big data, high-dimensional data, blended data, and missing values compared to traditional physical, chemical, and regression statistics [14].…”

“…Among these climatic factors, the most prominent influences include temperature, precipitation, and humidity [17]. Additionally, the role of vegetation in influencing the deposition and diffusion of pollutants by absorbing and trapping fine dust particles and gaseous pollutants also needs to be considered [20,31]. The search for effective predictors is an important preliminary work to help the government formulate effective air quality improvement strategies, determine the best implementation plan, and depict the air pollution process in the key step of virtual simulation scene modeling.…”