air pollution

Improving Brick Kiln Technology to Reduce Climate and Health Impacts (Revise & resubmit at Science)

Steady economic growth and large working-age populations are driving demand for bricks to support a decades long construction boom across South Asian countries. Coal-burning traditional brick kilns that operate in the informal sector produce the majority of these bricks and contribute substantially to global climate change and worsen local air quality.

Building blocks of change: The energy, health, and climate co-benefits of more efficient brickmaking in Bangladesh

We employed a multiphase, interdisciplinary, mixed-methods approach to identify solutions. In this paper, we first summarize past approaches and discuss the key barriers we identified to improving the industry, then we present the design, and results of a randomized pilot energy efficiency intervention designed to overcome barriers to improved kiln operation.

Health consequences of small-scale industrial pollution: Evidence from the brick sector in Bangladesh

The adverse health impacts of air pollution have been widely documented, yet there is little empirical evidence on the externalities of the brick manufacturing industry industry. We conducted a field study in Bangladesh to quantify the contribution of brick kilns to fine particulate matter (PM2.5) and respiratory health. We exploit variation in the timing of brick production, seasonal wind direction, and household proximity to kilns to isolate the effects of brick manufacturing from other sources of air pollution. Our findings suggest that existing regulations, which require that kilns be at least 1–2 km from residential areas, schools, and health facilities, are inadequate to protect nearby communities from the substantial health burden brick manufacturing imposes.

Scalable deep learning to identify brick kilns and aid regulatory capacity

Monitoring compliance with environmental regulations is a global challenge. It is particularly difficult for governments in low-income countries, where informal industry is responsible for a large amount of pollution, because the governments lack the ability to locate and monitor large numbers of dispersed polluters. This study demonstrates an accurate, scalable machine learning approach for identifying brick kilns, a highly polluting informal industry in Bangladesh, in satellite imagery.Our data reveal widespread violations of the national regulations governing brick manufacturing, which has implications for the health and well-being of the country. Our approach offers a low-cost, replicable method for regulatory agencies to generate information on key pollution sources.