Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors

There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers.&#160;Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r2 = 0.88), but have a mean bias of approximately 12 &#956;g m-3. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 &#956;g m-3 to -1.84 &#181;g m-3 or less and improve the the r2 from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries.&#160;&#160;&#160;

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Domain adaptation based deep calibration of low-cost PM2.5 sensors

10.36227/techrxiv.15276186 ◽

2021 ◽

Author(s):

Sonu Kumar Jha ◽

Mohit Kumar ◽

Vipul Arora ◽

Sachchida Nand Tripathi ◽

Vidyanand Motiram Motghare ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Domain Adaptation ◽

Low Cost ◽

Monitoring Network ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Proposed Model ◽

Temperature And Humidity ◽

Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b2-729-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 729-733 ◽

Cited By ~ 3

Author(s):

L. Marek ◽

M. Campbell ◽

M. Epton ◽

M. Storer ◽

S. Kingham

Keyword(s):

Quality Of Life ◽

Air Pollution ◽

Air Quality ◽

Real Time ◽

Low Cost ◽

Monitoring Network ◽

Environmental Data ◽

Chronic Obstructive ◽

Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

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Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

10.20944/preprints202006.0158.v1 ◽

2020 ◽

Author(s):

Eric S. Coker ◽

Ssematimba Joel ◽

Engineer Bainomugisha

Keyword(s):

Land Use ◽

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Low Cost ◽

Additive Models ◽

Pollution Monitoring ◽

Sub Saharan Africa ◽

Land Use Regression ◽

Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

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Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

10.5194/amt-2016-131 ◽

2016 ◽

Cited By ~ 5

Author(s):

Wan Jiao ◽

Gayle Hagler ◽

Ronald Williams ◽

Robert Sharpe ◽

Ryan Brown ◽

...

Keyword(s):

Air Pollution ◽

Carbon Monoxide ◽

Air Quality ◽

Sulfur Dioxide ◽

Sensor Network ◽

Correlation Coefficient ◽

Low Cost ◽

Ambient Air ◽

Sensor Technology ◽

Sensor Performance

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.

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High-resolution PM2.5 forecasting using CAMS predictions, low-cost sensors and ensemble techniques

10.5194/egusphere-egu21-15960 ◽

2021 ◽

Author(s):

Areti Pappa ◽

Ioannis Kioutsioukis

Keyword(s):

Air Pollution ◽

Air Quality ◽

Short Term Memory ◽

Low Cost ◽

Quality Models ◽

Ensemble Techniques ◽

Spatial Coverage ◽

Comparable Performance ◽

Lstm Network ◽

Air Quality Monitoring Stations

Expediting urbanization has triggered an increase in cardiopulmonary diseases attributable to fine-particulate air pollution. Air Quality models simulate the dilution and dispersion of air pollutants that affect the atmosphere, contributing crucially to the comprehension of its processes. Air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) provide open access to accurate and reliable information but in a coarse resolution. Data-driven models can downscale the forecasts from deterministic air quality models on the basis of reliable measurements. Low-cost air quality sensors are widely known for their increased spatial coverage and economic operational costs, but usually, their reliability is in dispute. In this study, a dense network of calibrated PM2.5 measurements installed in the city of Patras is combined with CAMS forecasts and statistical approaches to generate 24h forecasts of PM2.5 concentrations in an urban area of Greece. The implemented techniques are the analog ensemble (AnEn) and the Long Short-Term Memory (LSTM) network. Auxiliary variables of meteorological origin were also utilized. The required forecasts were retrieved from the European Center for Medium-Range Weather Forecasts (ECMWF), and were pin-pointed to the location of the air quality monitoring stations. The results showed that both methods had comparable performance, with low bias and relatively small errors. In the stations with high PM2.5 levels, AnEn performed better, whereas in the stations and seasons with moderate concentrations LSTM outperformed. A comprehensive validation is presented and discussed. AnEn and LSTM methods were proved reliable tools for air pollution forecasting and can be used for other regions with small modifications.

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Application of Low-Cost Air Quality Monitoring Sensor to Assess the Exposure of Ambient Air Pollution Due to PM2.5 and PM10

Lecture Notes in Civil Engineering - Sustainability in Environmental Engineering and Science ◽

10.1007/978-981-15-6887-9_16 ◽

2020 ◽

pp. 135-148

Author(s):

Md. Noman Munshi ◽

S. M. Nihab Ahsan ◽

Md. Shafinur Rahman ◽

M. Tauhid Ur Rahman

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Ambient Air ◽

Quality Monitoring ◽

Ambient Air Pollution ◽

Air Quality Monitoring ◽

Pm2.5 And Pm10

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Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya

Journal of Environmental and Public Health ◽

10.1155/2021/4258816 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Caroline Kiai ◽

Christopher Kanali ◽

Joseph Sang ◽

Michael Gatari

Keyword(s):

Land Use ◽

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Low Cost ◽

Airborne Particulate Matter ◽

Spatial Extent ◽

Airborne Particulate ◽

Concentration Levels ◽

Pm2.5 Concentration

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.

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Air pollution exposure monitoring using portable low-cost air quality sensors

Smart Health ◽

10.1016/j.smhl.2021.100241 ◽

2021 ◽

pp. 100241

Author(s):

Pranvera Korto¸ci ◽

Naser Hossein Motlagh ◽

Martha Arbayani Zaidan ◽

Pak Lun Fung ◽

Samu Varjonen ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Exposure Monitoring ◽

Air Pollution Exposure ◽

Pollution Exposure

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High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

10.5194/amt-2019-410 ◽

2019 ◽

Author(s):

Bas Mijling

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Urban Air Pollution ◽

Heterogeneous Data ◽

Urban Air Quality ◽

Quality Data ◽

Urban Air ◽

Reference Measurements ◽

Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

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