Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM_2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM_2.5 between sensors. PM_2.5 concentrations were also compared to traffic levels to assess the sensors' ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM_2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM_2.5 levels, indicating that indoor sensors detected PM_2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM_2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

Improving emissions inputs via mobile measurements to estimate fine-scale Black Carbon monthly concentrations through geostatistical space-time data fusion

Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R² from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R² values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R² for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.

Estimating Intra-Urban Inequities in PM2.5-Attributable Health Impacts: A Case Study for Washington, DC

Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM2.5)- attributable mortality and morbidity. We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite-derived annual mean surface PM2.5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5-attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources

The combination of air quality (AQ) data from satellites and low-cost sensor systems, along with output from AQ models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost-effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long-range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders.

Sub-City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

A Systematic Review of Air Quality Sensors, Guidelines, and Measurement Studies for Indoor Air Quality Management

The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the quantitative air quality guidelines and standards in order to meet the requirements for indoor air quality management. This paper first provides a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS); the World Health Organization (WHO); the Occupational Safety and Health Administration (OSHA); the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational Safety and Health (NIOSH); and the California ambient air quality standards (CAAQS). It then adds to this by providing a state-of-art review of the existing low-cost air quality sensor (LCAQS) technologies, and analyzes the corresponding specifications, such as the typical detection range, measurement tolerance or repeatability, data resolution, response time, supply current, and market price. Finally, it briefly reviews a sequence (array) of field measurement studies, which focuses on the technical measurement characteristics and their data analysis approaches.

Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors

Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44-0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire.

How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30' N, 11°21' E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.

Performance of a Low-Cost Sensor Community Air Monitoring Network in Imperial County, CA

Air monitoring networks developed by communities have potential to reduce exposures and affect environmental health policy, yet there have been few performance evaluations of networks of these sensors in the field. We developed a network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter. We report here on the performance of the Network to date by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015-2018) on a network-wide basis. Annual mean levels of PM10 did not differ statistically from regulatory annual means, but did for PM2.5 for two out of the 4 years. R2s from ordinary least square regression results ranged from 0.16 to 0.67 for PM10, and increased each year of operation. Sensor variability was higher among the Network monitors than the regulatory monitors. The Network identified a larger number of pollution episodes and identified under-reporting by the regulatory monitors. The participatory approach of the project resulted in increased engagement from local and state agencies and increased local knowledge about air quality, data interpretation, and health impacts. Community air monitoring networks have the potential to provide real-time reliable data to local populations.

Modeling of pollutant distribution based on mobile sensor networks

Pollution monitoring based on wireless sensor networks is becoming highly attractive. This paper presents an effective pollutant distribution modeling approach using a mobile sensor network. As for mobile nodes, energy consumption and link quality between nodes are two key factors. In the proposed approach, we present an autonomous sensing model and an energy-driven motion control scheme, which can make a good trade-off between energy efficiency and modeling accuracy. A comprehensive set of simulations demonstrate that our approach can model the pollutant distribution with less iteration times and higher accuracy. In particular, even for a relatively complex concentration field, the similarity between the reconstructed model and the pollutant distribution can reach 95% through about 20 iterations using 25 mobile sensor nodes. Moreover, we validated the feasibility of the proposed approach through an actual monitoring of water pollutant distribution.

Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models

Fine particulate matter (PM2.5) is a well-established risk factor for public health. To support both health risk assessment and epidemiological studies, data are needed on spatial and temporal patterns of PM2.5 exposures. This review article surveys publicly available exposure datasets for surface PM2.5 mass concentrations over the contiguous U.S., summarizes their applications and limitations, and provides suggestions on future research needs. The complex landscape of satellite instruments, model capabilities, monitor networks, and data synthesis methods offers opportunities for research development, but would benefit from guidance for new users. Guidance is provided to access publicly available PM2.5 datasets, to explain and compare different approaches for dataset generation, and to identify sources of uncertainties associated with various types of datasets. Three main sources used to create PM2.5 exposure data are ground-based measurements (especially regulatory monitoring), satellite retrievals (especially aerosol optical depth, AOD), and atmospheric chemistry models. We find inconsistencies among several publicly available PM2.5 estimates, highlighting uncertainties in the exposure datasets that are often overlooked in health effects analyses. Major differences among PM2.5 estimates emerge from the choice of data (ground-based, satellite, and/or model), the spatiotemporal resolutions, and the algorithms used to fuse data sources.Implications: Fine particulate matter (PM2.5) has large impacts on human morbidity and mortality. Even though the methods for generating the PM2.5 exposure estimates have been significantly improved in recent years, there is a lack of review articles that document PM2.5 exposure datasets that are publicly available and easily accessible by the health and air quality communities. In this article, we discuss the main methods that generate PM2.5 data, compare several publicly available datasets, and show the applications of various data fusion approaches. Guidance to access and critique these datasets are provided for stakeholders in public health sectors.

Experience from Integrated Air Quality Management in the Mexico City Metropolitan Area and Singapore

More than half of the world’s population now lives in cities as a result of unprecedented urbanization during the second half of the 20th century. The urban population is projected to increase to 68% by 2050, with most of the increase occurring in Asia and Africa. Population growth and increased energy consumption in urban areas lead to high levels of atmospheric pollutants that harm human health, cause regional haze, damage crops, contribute to climate change, and ultimately threaten the society’s sustainability. This article reviews the air quality and compares the policies implemented in the Mexico City Metropolitan Area (MCMA) and Singapore and offers insights into the complexity of managing air pollution to protect public health and the environment. While the differences in the governance, economics, and culture of the two cities greatly influence the decision-making process, both have made much progress in reducing concentrations of harmful pollutants by implementing comprehensive integrated air quality management programs. The experience and the lessons learned from the MCMA and Singapore can be valuable for other urban centers, especially in the fast-growing Asia-Pacific region confronting similar air pollution problems.

Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border

This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM2.5 and PMcoarse) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM2.5 and PMcoarse were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) R2 and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM2.5 (CV R2 = 0.47, RMSE = 1.5 µg/m3) and PMcoarse (CV R2 = 0.65, RMSE = 8.07 µg/m3). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m3 for PM2.5 and 17.7 µg/m3 for PMcoarse. Variable importance measures pointed to seasonality and length of roads as drivers of PM2.5, and seasonality, type of farmland, and length of roads as drivers of PMcoarse. Predicted PM2.5 was elevated near the US/Mexico border and predicted PMcoarse was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities.

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.