Opportunities and challenges for filling the air quality data gap in low- and middle-income countries

Pinder, R. W.; Klopp, Jacqueline M.; Kleiman, Gary; Hagler, Gayle; Awe, Yewande; Terry, Sara

doi:10.1016/j.atmosenv.2019.06.032

Cited by 66 publications

(40 citation statements)

References 26 publications

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“…However, due to the large spatial-temporal heterogeneity of air pollutants in urban areas resulting from variations in emission sources and atmospheric transport [ 17 , 18 ], it is difficult to monitor pollutants at finer resolutions, such as at the street scale, which is an urgent need for both MEE grid level reductions for fine management and the public need for local pollution information. With the development of low-cost electrochemical air quality sensors, this monitoring has become possible in recent years, especially for low- and middle-income countries [ 19 ]. Low-cost sensors integrated in Internet of Things (IoT) applications are operated in dense sensor networks for urban air quality monitoring of pollutant gases, particulate matter, and greenhouse gases with high spatial and temporal resolutions [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Han

Liu

et al. 2021

Sensors

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Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

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Section: Introductionmentioning

confidence: 99%

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Han

Liu

et al. 2021

Sensors

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“…The world health organization (WHO) estimates that there were 4.9 million of premature deaths that caused by ambient air pollution in 2017 (Health Effects Institute and Project, 2019). This condition continues to increase every year at an alarming rate around the world, especially in low and middle-income countries (Pinder et al, 2019;Gulia et al, 2020;Antoine et al, 2019). Over the last two decades, Indonesia has experienced very significant changes in its air quality during the period 1998 to 2016; Indonesia is one of the twenty most polluted countries in the world (Greenstone and Fan, 2019).…”

Section: Introductionmentioning

confidence: 99%

Carbon Emissions, Economic Growth, Forest, Agricultural Land and Air Pollution in Indonesia

Azwardi

Sukanto

Igamo

et al. 2021

IJEEP

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This study specifically analyzes the effect of motor vehicle emissions, economic growth of forest area, and agricultural area on air pollution in 33 Indonesian Provinces during the year 2010-2017 using the panel data regression approach. The results showed that (1) Carbon emissions and agricultural area in all regions in Indonesia had a negative and significant effect on the air quality index (2) Based on the regions of Sumatra Island, Java Island, and Kalimantan Island, GDP had a positive and insignificant effect on air pollution (3) Forest area in all regions in Indonesia, as a whole, has a positive and significant effect on the air quality index.

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“…Apte et al, 2017;Clark et al, 2014;Miller et al, 2020;Shah et al, 2020). Many of the world's most populous and polluted regions are also those with limited air quality monitoring infrastructure, restricting the potential for data-driven air quality management or public awareness campaigns (Pinder et al, 2019). Even in many high-income countries, ambient air pollution monitoring is relatively sparse (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluating uncertainty in sensor networks for urban air pollution insights

Peters

Popoola

Jones

et al. 2021

Preprint

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Abstract. Ambient air pollution poses a major global public health risk. Lower-cost air quality sensors (LCS) are increasingly being explored as a tool to understand local air pollution problems and develop effective solutions. A barrier to LCS adoption is potentially larger measurement uncertainty compared to reference measurement technology. The technical performance of various LCS has been tested in laboratory and field environments, and a growing literature on uses of LCS primarily focuses on proof-of-concept deployments. However, few studies have demonstrated the implications of LCS measurement uncertainties on a sensor network’s ability to assess spatiotemporal patterns of local air pollution. Here, we present results from a 2-year deployment of 100 stationary electrochemical nitrogen dioxide (NO2) LCS across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with reference instruments, estimating ~35 % average uncertainty (root-mean-square error) of the calibrated LCS, and identified infrequent, multi-week periods of poorer performance and high bias during summer months. We analyzed BL data to generate insights about London’s air pollution, including long-term concentration trends, diurnal and day-of-week patterns, and profiles of elevated concentrations during regional pollution episodes. These findings were validated against measurements from an extensive reference network, demonstrating the BL network’s ability to generate robust information about London’s air pollution. In cases where the BL network did not effectively capture features that the reference network measured, ongoing collocations of representative sensors often provided evidence of irregularities in sensor performance, demonstrating how, in the absence of an extensive reference network, project-long collocations could enable characterization and mitigation of network-wide sensor uncertainties. The conclusions are restricted to the specific sensors used for this study, but the results give direction to LCS users by demonstrating the kinds of air pollution insights possible from LCS networks and provide a blueprint for future LCS projects to manage and evaluate uncertainties when collecting, analyzing and interpreting data.

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Opportunities and challenges for filling the air quality data gap in low- and middle-income countries

Cited by 66 publications

References 26 publications

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Carbon Emissions, Economic Growth, Forest, Agricultural Land and Air Pollution in Indonesia

Evaluating uncertainty in sensor networks for urban air pollution insights

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