A Spatiotemporal, Quasi-experimental Causal Inference Approach to Characterize the Effects of Global Plastic Waste Export and Burning on Air Quality Using Remotely Sensed Data

Open burning of plastic waste may pose a significant threat to global health by degrading air quality, but quantitative research on this problem -- crucial for policy making -- has been stunted by lack of data. Critically, many low- and middle-income countries, where open burning is of greatest concern, have little to no air quality monitoring. Here, we propose an approach to leverage remotely sensed data products combined with spatiotemporal causal analytic techniques to evaluate the impact of large-scale plastic waste policies on air quality. Throughout, we use the case study of Indonesia before and after 2018, when China halted its import of plastic waste, resulting in diversion of this massive waste stream to other countries. We tailor cutting-edge statistical methods to this setting, estimating effects of the increase in plastic waste imports on fine particulate matter (PM$_{2.5}$) near waste dump sites in Indonesia as a function of proximity to ports, which serves as an induced continuous exposure. We observe that dump sites above the 20th quantile of port proximity experienced a statistically significant increase in monthly PM$_{2.5}$ concentrations after China's ban took effect (2018-2019) compared to concentrations expected under business-as-usual (2012-2017), with increases ranging from 0.76--1.72$\mu$g/m$^3$.