Opportunities and challenges for filling the air quality data gap in low- and middle-income countries
Pinder, R., J. Klopp, G. Kleiman, G. Hagler, Y. Awe, AND S. Terry. Opportunities and challenges for filling the air quality data gap in low- and middle-income countries. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 215:116794, (2019). https://doi.org/10.1016/j.atmosenv.2019.06.032
This paper provides an overview on the state of air monitoring data in low and middle income countries (LMICs), synthesizing the findings from two workshops that EPA co-convened over the past 2 years.
This perspectives article assesses the current state of air monitoring in low and middle income countries (LMICs) and concludes with the following specific recommendations: 1) Provide clear, informed guidance to LMICs on purchasing and deploying low-cost sensor devices, alongside a smaller number of higher quality, higher cost devices. Together, these could provide a wealth of data for decisionmakers in LMICs. For example, where can LMICs find trusted, transparent information related to device data ownership, device data quality over time in different environments, device replacement frequency, device calibration, siting, equipment costs, data management and analysis, and expenses for operations and maintenance? 2) Develop siting protocols relevant to sensor networks that support greater spatial density and more frequent calibration needs. 3) Conduct field testing in settings representative of LMIC conditions. What is the range of performance for emerging sensor technologies under the diversity of environmental and pollution conditions representative of low- and middle-income countries? 4) Design instruments that could continue to operate during times of intermittent power and data connectivity. 5) Develop and share best practices in sensor data management, considering issues that have been described in greater depth elsewhere (Hagler et al., 2018) such as defensible approaches to post-process data, data integrity and transparency. Develop open source software tools for archiving, interpreting, and communicating data from sensor networks. 6) Invest in the responsible air quality staff within LMICs to develop and maintain sustained air monitoring infrastructure. This includes staff training, professional regional networks for sharing best practices, shared data platforms, and supply chain viability for equipment and consumables. 7) Support the development of lasting institutions for creating and dispersing air quality information, including building public support for sustained, credible monitoring. Foster the participation of LMIC stakeholders in these institutions. 8) Strongly encourage the public availability of air quality information (Hasenkopf et al., 2016). As articulated in the mission of OpenAQ, “air pollution is one of the greatest environmental health issues of our time and opening up these data is a powerful step forward in our collective progress to defeat it. ”