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Over six million people die prematurely each year from exposure to air pollution. Current air quality metrics insufficiently monitor exposure to air pollutants. This gap hinders the ability of decisionmakers to address the public health impacts of air pollution. To spur new emissions control policies and ensure implemented solutions realize meaningful gains in environmental health, we develop a framework of public-health-focused air quality indicators that quantifies over 200 countries’ trends in exposure to particulate matter, ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds. We couple population density to ground-level pollutant concentrations to derive population-weighted exposure metrics that quantify the pollutant levels experienced by the average resident in each country. Our analyses demonstrate that most residents in 171 countries experience pollutant levels exceeding international health guidelines. In addition, we find a negative correlation between temporal trends in ozone and nitrogen oxide concentrations, whichwhen qualitatively interpreted with a simple atmospheric chemistry box modelcan help describe the apparent tradeoff between the mitigation of these two pollutants on local scales. These novel indicators and their applications enable regulators to identify their most critical pollutant exposure trends and allow countries to track the performance of their emission control policies over time.

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This article offers an assessment of current data practices in the citizen science, community science, and crowdsourcing communities. We begin by reviewing current trends in scientific data relevant to citizen science before presenting the results of our qualitative research. Following a purposive sampling scheme designed to capture data management practices from a wide range of initiatives through a landscape sampling methodology (Bos et al. 2007), we sampled 36 projects from English-speaking countries. The authors used a semi-structured protocol to interview project proponents (either scientific leads or data managers) to better understand how projects are addressing key aspects of the data lifecycle, reporting results through descriptive statistics and other analyses. Findings suggest that citizen science projects are doing well in terms of data quality assessment and governance, but are sometimes lacking in providing open access to data outputs, documenting data, ensuring interoperability through data standards, or building robust and sustainable infrastructure. Based on this assessment, the paper presents a number of recommendations for the citizen science community related to data quality, data infrastructure, data governance, data documentation, and data access.

Outcome-based equity relates to the consequences of a policy, action, or developmental trend, which is acknowledged to be important for both low-and high-income countries. Procedural equity refers to impartiality and fairness in the process of delivering and administering justice (Shukla, 1999). This is more often discussed in relation to urban climate initiatives in low-income