Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5

Li, Jiayu; Zhang, Huang; Chao, Chun Ying; Chien, Chih-Hsiang; Wu, Chang Yu; Luo, Cyuan Heng; Chen, Ling-Jyh; Biswas, Pratim

doi:10.1016/j.atmosenv.2020.117293

Cited by 76 publications

(39 citation statements)

References 51 publications

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“…Networks of low-cost nephelometers (notably the Plantower PMS5003) have been suggested and deployed in large numbers as a means to provide surface PM 2.5 data at a higher spatial density than can be achieved with reference-grade monitors (Lin et al, 2020;Li et al, 2020;Badura et al, 2020;Lu et al, 2021;Chadwick et al, 2021). However, low-cost sensors (or more specifically, the Plantower PMS5003 devices) tend to exhibit measurement bias (Kelly et al, 2017;Zheng et al, 2018;Levy Zamora et al, 2019;Sayahi et al, 2019;Tryner et al, 2020), requiring correction relative to reference monitors (Ford et al, 2019;Wendt et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements

et al. 2021

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Abstract. Atmospheric particulate matter smaller than 2.5 µm in diameter (PM2.5) has a negative impact on public health, the environment, and Earth's climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM2.5 concentrations at a global scale. Existing PM2.5 measurement infrastructure provides broad PM2.5 sampling coverage but does not adequately characterize community-level air pollution at high temporal resolution. This motivates the development of low-cost sensors which can be more practically deployed in spatial and temporal configurations currently lacking proper characterization. Wendt et al. (2019) described the development and validation of a first-generation device for low-cost measurement of AOD and PM2.5: the Aerosol Mass and Optical Depth (AMODv1) sampler. Ford et al. (2019) describe a citizen-science field deployment of the AMODv1 device. In this paper, we present an updated version of the AMOD, known as AMODv2, featuring design improvements and extended validation to address the limitations of the AMODv1 work. The AMODv2 measures AOD and PM2.5 at 20 min time intervals. The sampler includes a motorized Sun tracking system alongside a set of four optically filtered photodiodes for semicontinuous, multiwavelength (current version at 440, 500, 675, and 870 nm) AOD sampling. Also included are a Plantower PMS5003 sensor for time-resolved optical PM2.5 measurements and a pump/cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. AMODv2 samples are configured using a smartphone application, and sample data are made available via data streaming to a companion website (https://csu-ceams.com/, last access: 16 July 2021). We present the results of a 9 d AOD validation campaign where AMODv2 units were co-located with an AERONET (Aerosol Robotics Network) instrument as the reference method at AOD levels ranging from 0.02 ± 0.01 to 1.59 ± 0.01. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.04, 0.06, 0.03, and 0.03 AOD units at 440, 500, 675, and 870 nm, respectively. We derived empirical relationships relating the reference AOD level to AMODv2 instrument error and found that the mean absolute error in the AMODv2 deviated by less than 0.01 AOD units between clear days and elevated-AOD days and across all wavelengths. We identified bias from individual units, particularly due to calibration drift, as the primary source of error between AMODv2s and reference units. In a test of 15-month calibration stability performed on 16 AMOD units, we observed median changes to calibration constant values of −7.14 %, −9.64 %, −0.75 %, and −2.80 % at 440, 500, 675, and 870 nm, respectively. We propose annual recalibration to mitigate potential errors from calibration drift. We conducted a trial deployment to assess the reliability and mechanical robustness of AMODv2 units. We found that 75 % of attempted samples were successfully completed in rooftop laboratory testing. We identify several failure modes in the laboratory testing and describe design changes that we have since implemented to reduce failures. We demonstrate that the AMODv2 is an accurate, stable, and low-cost platform for air pollution measurement. We describe how the AMODv2 can be implemented in spatial citizen-science networks where reference-grade sensors are economically impractical and low-cost sensors lack accuracy and stability.

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Section: Introductionmentioning

confidence: 99%

A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements

et al. 2021

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“…This introduces underestimation when more monitoring sites are located in the rural and less populated areas and overestimation when monitoring sites are more likely to be located in urban and populous areas for a city. Ways to improve the spatial accuracy of health impact estimation include utilizing population distribution with high spatial resolution and interpolated PM 2.5 concentrations based on networks of low‐cost sensors (Cavaliere et al, 2018; J. Li et al, 2020; Holstius et al, 2014) or satellite based data (e.g., the aerosol optical depth, Chen et al, 2019; J. Li et al, 2020). Finally, estimations of avoided premature death assuming no‐lockdown are associated with uncertainties with the meteorology and air quality model.…”

Section: Resultsmentioning

confidence: 99%

The Silver Lining of COVID‐19: Estimation of Short‐Term Health Impacts Due to Lockdown in the Yangtze River Delta Region, China

Huang

Liu

et al. 2020

GeoHealth

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The outbreak of COVID-19 in China has led to massive lockdowns in order to reduce the spread of the epidemic and control human-to-human transmission. Subsequent reductions in various anthropogenic activities have led to improved air quality during the lockdown. In this study, we apply a widely used exposure-response function to estimate the short-term health impacts associated with PM 2.5 changes over the Yangtze River Delta (YRD) region due to COVID-19 lockdown. Concentrations of PM 2.5 during lockdown period reduced by 22.9% to 54.0% compared to pre-lockdown level. Estimated PM 2.5related daily premature mortality during lockdown period is 895 (95% confidential interval: 637-1,081), which is 43.3% lower than pre-lockdown period and 46.5% lower compared with averages of 2017-2019. According to our calculation, total number of avoided premature death aassociated with PM 2.5 reduction during the lockdown is estimated to be 42.4 thousand over the YRD region, with Shanghai, Wenzhou, Suzhou (Jiangsu province), Nanjing, and Nantong being the top five cities with largest health benefits. Avoided premature mortality is mostly contributed by reduced death associated with stroke (16.9 thousand, accounting for 40.0%), ischemic heart disease (14.0 thousand, 33.2%), and chronic obstructive pulmonary disease (7.6 thousand, 18.0%). Our calculations do not support or advocate any idea that pandemics produce a positive note to community health. We simply present health benefits from air pollution improvement due to large emission reductions from lowered human and industrial activities. Our results show that continuous efforts to improve air quality are essential to protect public health, especially over city-clusters with dense population. Plain Language Summary The outbreak of COVID-19 is tragic and has caused tremendous impacts on people's life across the world. In late January 2020, massive lockdown was taken in China in order to prevent the spread of the pandemic and control human-to-human transmissions. As a result of reduced human activities, air quality was shown to improve substantially over China. A natural follow-up question would be what is the short-term health impact associated with the changes of air quality during the lockdown. This study attempted to quantify the number of avoided death due to reduced PM 2.5 concentrations over the Yangtze River Delta Region in China, a region with dense population and used to suffer from severe air pollution problems in the past.

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“…The air quality sensor can detect the air quality of the surrounding environment. Li et al [24] found that mining PM sensor data can not only retain the characteristics of pollutants, but also greatly enhance the spatial distribution of pollutants. Through research, Dewinter et al [25] introduced the change of PM 2.5 concentration within 20 m on both sides of the road.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories

Gao

2021

Sustainability

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Air pollution originating from anthropogenic emission, which is an important factor for environmental policy to regulate the sustainable development of enterprises and the environment. However, the missing or mislabeled discharge data make it impossible to apply this strategy in practice. In order to solve this challenge, we firstly discover that the energy consumption in a factory and the air pollutants are linearly related. Given this observation, we propose a support vector regression based Single-location recovery model to recover the air pollutant emission by using the energy consumption data in a factory. To further improve the precision of air pollutant emission estimation, we proposed a Gaussian process regression based multiple-location recovery model to estimate and recover the missing or mislabeled air pollutant emission from surrounding available air quality readings, collected by the government’s air quality monitoring station. Moreover, we optimally combine the two approaches to achieve the accurate air air pollutant emission estimation. To our best of knowledge, this is the first paper for monitoring the air pollutant emission taking both a factory’s energy consumption and government’s air quality readings into account. The research model in this article uses actual data(10,406,880 entries of data including weather, PM 2.5, date, etc.) from parts of Shandong Province, China. The dataset contains 33 factories (5 types) and we use the co-located air quality monitoring station as ground truth. The results show that, our proposed single-location recovery, multi-location recovery, and combined method could acquire the mean absolute error of 8.45, 9.69, and 7.25, respectively. The method has consistent accurate prediction behavior among 5 different factory types, shows a promising potential to be applied in broader locations and application areas, and outperforms the existing spatial interpolation based methods by 43.8%.

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Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5

Cited by 76 publications

References 51 publications

A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements

A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements

The Silver Lining of COVID‐19: Estimation of Short‐Term Health Impacts Due to Lockdown in the Yangtze River Delta Region, China

Multimodal Data Based Regression to Monitor Air Pollutant Emission in Factories

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