On the feasibility of measuring urban air pollution by wireless distributed sensor networks

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

Evaluating the performance and influencing factors of three portable black carbon monitors for field measurement

Black carbon (BC) is associated with adverse human health and climate change. Mapping BC spatial distribution imperatively requires low-cost and portable devices. Several portable BC monitors are commercially available, but their accuracy and reliability are not always satisfactory during continuous field observation. This study evaluated three models of portable black carbon monitors, C12, MA350 and DST, and investigates the factors that affect their performance. The monitors were tested in urban Beijing, where portable devices running for one month alongside a regular-size reference aethalometer AE33. The study considers several factors that could influence the monitors' performance, including ambient weather, aerosol composition, loading artifacts, and built-in algorithms. The results show that MA350 and DST present considerable discrepancies to the reference instrument, mainly occurring at lower concentrations (0-500 ng/m3) and higher concentrations (2500-8000 ng/m3), respectively. These discrepancies were likely caused by the anomalous noise of MA350 and the loading artifacts of DST. The study also suggests that the ambient environment has limited influence on the monitors' performance, but loading artifacts and accompanying compensation algorithms can result in unrealistic data. Based on the evaluation, the study suggests that C12 is the best choice for unsupervised field measurement, DST should be used in scenarios where frequent maintenance is available, and MA350 is suitable for research purposes with post-processing applicable. The study highlights the importance of assigning portable BC monitors to appropriate applications and the need for optimized real-time compensation algorithms.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment

BACKGROUND

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

OBJECTIVE

Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation.

METHODS

We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.

RESULTS

The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 andR 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-R 2  = 0.51 (with LCS).

IMPACT

We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment

The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.

Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors

Conventional air quality monitoring has been traditionally carried out in a few fixed places with expensive measuring equipment. This results in sparse spatial air quality data, which do not represent the real air quality of an entire area, e.g., when hot spots are missing. To obtain air quality data with higher spatial and temporal resolution, this research focused on developing a low-cost network of cloud-based air quality measurement platforms. These platforms should be able to measure air quality parameters including particulate matter (PM10, PM2.5, PM1) as well as gases like NO, NO2, O3, and CO, air temperature, and relative humidity. These parameters were measured every second and transmitted to a cloud server every minute on average. The platform developed during this research used one main computer to read the sensor data, process it, and store it in the cloud. Three prototypes were tested in the field: two of them at a busy traffic site in Stuttgart, Marienplatz and one at a remote site, Ötisheim, where measurements were performed near busy railroad tracks. The developed platform had around 1500 € in materials costs for one Air Quality Sensor Node and proved to be robust during the measurement phase. The notion of employing a Proportional-Integral-Derivative (PID) controller for the efficient working of a dryer that is used to reduce the negative effect of meteorological parameters such as air temperature and relative humidity on the measurement results was also pursued. This is seen as one way to improve the quality of data captured by low-cost sensors.

Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India

Mobile monitoring provides robust measurements of air pollution. However, resource constraints often limit the number of measurements so that assessments cannot be obtained in all locations of interest. In response, surrogate measurement methodologies, such as videos and images, have been suggested. Previous studies of air pollution and images have used static images (e.g., satellite images or Google Street View images). The current study was designed to develop deep learning methodologies to infer on-road pollutant concentrations from videos acquired with dashboard cameras. Fifty hours of on-road measurements of four pollutants (black carbon, particle number concentration, PM2.5 mass concentration, carbon dioxide) in Bengaluru, India, were analyzed. The analysis of each video frame involved identifying objects and determining motion (by segmentation and optical flow). Based on these visual cues, a regression convolutional neural network (CNN) was used to deduce pollution concentrations. The findings showed that the CNN approach outperformed several other machine learning (ML) techniques and more conventional analyses (e.g., linear regression). The CO2 prediction model achieved a normalized root-mean-square error of 10-13.7% for the different train-validation division methods. The results here thus contribute to the literature by using video and the relative motion of on-screen objects rather than static images and by implementing a rapid-analysis approach enabling analysis of the video in real time. These methods can be applied to other mobile-monitoring campaigns since the only additional equipment they require is an inexpensive dashboard camera.

Exposure to indoor and outdoor air pollution in schools in Africa: Current status, knowledge gaps, and a call to action

Chronic exposure to indoor and outdoor air pollution is linked to adverse human health impacts worldwide, and in children, these include increased respiratory symptoms, reduced cognitive and academic performance, and absences from school. African children are exposed to high levels of air pollution from aging diesel and gasoline second-hand vehicles, dusty roads, trash burning, and solid-fuel combustion for cooking. There is a need for more empirical evidence on the impact of air pollutants on schoolchildren in most countries of Africa. Therefore, we conducted a scoping review on schoolchildren's exposure to indoor and outdoor PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm and PM10 (particulate matter with an aerodynamic diameter less than 10 μm) in Africa. Following PRISMA guidelines, our search strategy yielded 2975 records, of which eight peer-reviewed articles met our selection criteria and were considered in the final analysis. We also analyzed satellite data on PM2.5 and PM10 levels in five African regions from 1990 to 2019 and compared schoolchildren's exposure to PM2.5 and PM10 levels in Africa with available data from the rest of the world. The findings showed that schoolchildren in Africa are frequently exposed to PM2.5 and PM10 levels exceeding the recommended World Health Organization air quality guidelines. We conclude with a list of recommendations and strategies to reduce air pollution exposure in African schools. Education can help to produce citizens who are literate in environmental science and policy. More air quality measurements in schools and intervention studies are needed to protect schoolchildren's health and reduce exposure to air pollution in classrooms across Africa.

Recent trends in ozone sensing technology

The harmful impact of ozone on humans and the environment makes the development of economical, accurate, and efficient ozone monitoring technologies necessary. Therefore, in the present review, we critically discuss developments in the methods for the synthesis of ozone sensing materials such as metal oxides (Ni, Co, Pd, In, Cu, Zn, Fe, Sn, W, Ti and Mo), carbon nanotubes, organic compounds, perovskites, and quartz. Additionally, the recent advancements and innovations in ozone technology will be discussed. In this review, we focus on assembling ozone-sensing devices and developing related wireless communication, data transferring, and analyzing technologies together with satellite, airborne, and ground-based novel ozone-sensing strategies for monitoring the atmosphere, urban areas, and working environments. Furthermore, the developments in ozone-monitoring miniaturized devices technology will be considered. The effects of different factors, such as spatial-temporal variation, humidity, and calibration, on ozone measurements will also be discussed. It is anticipated that this review will bridge the knowledge gaps among materials chemists, engineers, and industry.

Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana

The emergence of low-cost sensors for atmospheric observations presents a new opportunity for identifying atmospheric emission sources based on high-resolution data reporting. Low-cost sensors have been widely assessed for use in source monitoring and identification of hotspots of key atmospheric species in advanced countries (e.g., for CO, NOx, CO₂, SO₂, O₃, VOCs and PM (PM₁₀, PM_2.5 including emerging PM₁). In contrast, research in recent years has focused on their utility for real-time monitoring, understanding precision and associated calibration requirements in technologically lagging environments. This leads to limited evidence on the utility of high-resolution data from low-cost sensor networks for air pollution source identification in Ghana and more widely across the African continent. In this paper, we demonstrate the potential of low-cost sensors for emission source apportionment in urban areas of Ghana when used with analytical tools such as sectoral and cluster analysis. With a 14-week dataset from a low-cost sensor deployment study at Cape Coast in the Central Region of Ghana, we aimed to identify sources of particulate matter (PM_2.5 and PM₁₀). PM pollution was local (associated with increased PM at wind speeds of ≤2 m s^-1). High levels of PM during this study were associated with transport from the NNE. For coarse PM, hourly levels as high as 125 μg m^-3 were observed at higher wind speeds (7-8 m s^-1) indicating the importance of meteorology in the transport of PM. This study suggests that low-cost sensors could be deployed to (1) augment any existing sparsely distributed air quality monitoring and (2) undertake air quality monitoring for source apportionment studies in areas with no existing air quality observational capability (with appropriate calibration and operation in both cases). The urban landscape monitored in this study is typical of both Ghana and wider areas across Sub-Saharan Africa demonstrating the reproducibility of this study.

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

Comparative Study on the Use of Some Low-Cost Optical Particulate Sensors for Rapid Assessment of Local Air Quality Changes

Official air quality (AQ) stations are sporadically located in cities to monitor the anthropogenic pollutant levels. Consequently, their data cannot be used for further locations to estimate hidden changes in AQ and local emissions. Low-cost sensors (LCSs) of particulate matter (PM) in a network can help in solving this problem. However, the applicability of LCSs in terms of analytical performance requires careful evaluation. In this study, two types of pocket-size LCSs were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min). These devices utilize the method of laser irradiation and multi-angle light scattering on air-suspended particulates. A research-grade AQ monitor was applied as a reference. The LCSs showed acceptable accuracy for PM species in indoor/outdoor air even without calibration. Low PM readings (<10 μg/m3) were generally handicapped by higher bias, even between sensors of the same type. The relative humidity (RH) slightly affected the PM readings of LCSs at RHs higher than 85%, necessitating field calibration. The air quality index was calculated to classify the extent of air pollution and to make predictions for human health effects. The LCSs were useful for detecting peaks stemming from emissions of motor vehicular traffic and residential cooking/heating activities.

Optimizing Urban Air Pollution Detection Systems

Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

Information Theory Solution Approach to the Air Pollution Sensor Location-Allocation Problem

Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location-allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations' informativity. This entropy method is compared to two commonly used heuristics for solving the location-allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors' network measurements.

Low- and Medium-Cost Sensors for Tropospheric Ozone Monitoring—Results of an Evaluation Study in Wrocław, Poland

The paper presents the results of a 1.5-year evaluation study of low- and medium-cost ozone sensors. The tests covered electrochemical sensors: SensoriC O3 3E 1 (City Technology) and semiconductor gas sensors: SM50 OZU (Aeroqual), SP3-61-00 (FIS) and MQ131 (Winsen). Three copies of each sensor were enclosed in a measurement box and placed near the reference analyser (MLU 400). In the case of SensoriC O3 3E 1 sensors, the R2 values for the 1-h data were above 0.90 for the first 9 months of deployment, but a performance deterioration was observed in the subsequent months (R2 ≈ 0.6), due to sensor ageing processes. High linear relationships were observed for the SM50 devices (R2 > 0.94), but some periodic data offsets were reported, making regular checking and recalibration necessary. Power-law functions were used in the case of SP3-61-00 (R2 = 0.6–0.7) and MQ131 (R2 = 0.4–0.7). Improvements in the fittings were observed for models that included temperature and relative humidity data. In the case of SP3-61-00, the R2 values increased to above 0.82, while for MQ131 they increased to above 0.86. The study also showed that the measurement uncertainty of tested sensors meets the EU Directive 2008/50/EC requirements for indicative measurements and, in some cases, even for fixed measurements.

Exposome mapping in chronic respiratory diseases: the added value of digital technology

PURPOSE OF REVIEW

The development and progression of chronic respiratory diseases are impacted by a complex interplay between genetic, microbial, and environmental factors. Here we specifically summarize the effects of environmental exposure on asthma, allergic rhinitis, and chronic rhinosinusitis. We furthermore discuss how digital health technology may aid in the assessment of the environmental exposure of patients and how it may be of added value for them.

RECENT FINDINGS

It is well established that one gets allergic symptoms if sensitized and exposed to the same allergen. Viruses, bacteria, pollutants, irritants, and lifestyle-related factors modify the risk of getting sensitized and develop symptoms or may induce symptoms themselves. Understanding these processes and how the various factors interact with each other and the human body require big data and advanced statistics. Mobile health technology enables integration of multiple sources of data of the patients' exposome and link these to patient outcomes. Such technologies may contribute to the increased understanding of the development of chronic respiratory disease.

SUMMARY

Implementation of digital technologies in clinical practice may in future guide the development of preventive strategies to tackle chronic respiratory diseases and eventually improve outcomes of the patient.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO₂) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO₂ sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O₃ and temperature in addition to a reference NO₂ measurement, improves the prediction in terms of R² from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

Flame-Made La2O3-Based Nanocomposite CO2 Sensors as Perspective Part of GHG Monitoring System

Continuous monitoring of greenhouse gases with high spatio-temporal resolution has lately become an urgent task because of tightening environmental restrictions. It may be addressed with an economically efficient solution, based on semiconductor metal oxide gas sensors. In the present work, CO₂ detection in the relevant concentration range and ambient conditions was successfully effectuated by fine-particulate La₂O₃-based materials. Flame spray pyrolysis technique was used for the synthesis of sensitive materials, which were studied with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) and low temperature nitrogen adsorption coupled with Brunauer–Emmett–Teller (BET) effective surface area calculation methodology. The obtained materials represent a composite of lanthanum oxide, hydroxide and carbonate phases. The positive correlation has been established between the carbonate content in the as prepared materials and their sensor response towards CO₂. Small dimensional planar MEMS micro-hotplates with low energy consumption were used for gas sensor fabrication through inkjet printing. The sensors showed highly selective CO₂ detection in the range of 200–6667 ppm in humid air compared with pollutant gases (H₂ 50 ppm, CH₄ 100 ppm, NO₂ 1 ppm, NO 1 ppm, NH₃ 20 ppm, H₂S 1 ppm, SO₂ 1 ppm), typical for the atmospheric air of urbanized and industrial area.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO₂; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O₃) and NO₂. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R² measures (CO: RMSE = 18 ppb, R² = 0.97; NO: RMSE = 2 ppb, R² = 0.97). Performance measures for NO₂ and O₃ were somewhat lower (NO₂: RMSE = 3 ppb, R² = 0.79; O₃: RMSE = 4 ppb, R² = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors

Air pollution, especially fine particulate matter (PM2.5), is a major environmental risk factor for human health in Europe. Monitoring of air quality takes place using expensive reference stations. Low-cost sensors are a promising addition to this official monitoring network as they add spatial and temporal resolution at low cost. Moreover, low-cost sensors might allow for better characterization of personal exposure to PM2.5. In this study, we use 500 dust (PM2.5) sensors mounted on bicycles to estimate typical PM2.5 levels to which cyclists are exposed in the province of Utrecht, the Netherlands, in the year 2020. We use co-located sensors at reference stations to calibrate and validate the mobile sensor data. We estimate that the average exposure to traffic related PM2.5, on top of background concentrations, is approximately 2 μg/m3. Our results suggest that cyclists close to major roads have a small, but consistently higher exposure to PM2.5 compared to routes with less traffic. The results allow for a detailed spatial representation of PM2.5 concentrations and show that choosing a different cycle route might lead to a lower exposure to PM2.5. Finally, we conclude that the use of mobile, low-cost sensors is a promising method to estimate exposure to air pollution.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM_2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM_2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM_2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM_2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Remote Tracking Gas Molecular via the Standalone-Like Nanosensor-Based Tele-Monitoring System

HIGHLIGHTS

A standalone-like smart device that can remotely track the variation of air pollutants in a power-saving way is created; Metal–organic framework-derived hollow polyhedral ZnO was successfully synthesized, allowing the created smart device to be highly selective and to sensitively track the variation of NO₂ concentration; A novel photoluminescence-enhanced Li-Fi telecommunication technique is proposed, offering the created smart device with the capability of long distance wireless communication.

ABSTRACT

Remote tracking the variation of air quality in an effective way will be highly helpful to decrease the health risk of human short- and long-term exposures to air pollution. However, high power consumption and poor sensing performance remain the concerned issues, thereby limiting the scale-up in deploying air quality tracking networks. Herein, we report a standalone-like smart device that can remotely track the variation of air pollutants in a power-saving way. Brevity, the created smart device demonstrated satisfactory selectivity (against six kinds of representative exhaust gases or air pollutants), desirable response magnitude (164–100 ppm), and acceptable response/recovery rate (52.0/50.5 s), as well as linear response relationship to NO₂. After aging for 2 weeks, the created device exhibited relatively stable sensing performance more than 3 months. Moreover, a photoluminescence-enhanced light fidelity (Li-Fi) telecommunication technique is proposed and the Li-Fi communication distance is significantly extended. Conclusively, our reported standalone-like smart device would sever as a powerful sensing platform to construct high-performance and low-power consumption air quality wireless sensor networks and to prevent air pollutant-induced diseases via a more effective and low-cost approach. [Image: see text]

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (10.1007/s40820-020-00551-w) contains supplementary material, which is available to authorized users.

Long-term calibration models to estimate ozone concentrations with a metal oxide sensor

Ozone (O₃) is a potent oxidant associated with adverse health effects. Low-cost O₃ sensors, such as metal oxide (MO) sensors, can complement regulatory O₃ measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM_2.5 concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O₃ although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O₃ federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O₃ concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O₃ concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU's MO oxidizing species and temperature measurements and DAQ's solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R² = 0.491), and the ANN exhibited good performance (R² = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O₃ for five months after the calibration period and the results showed moderate correlations (R²s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O₃ levels.

Estimating personal exposures from a multi-hazard sensor network

Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O₃), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m³, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O₃, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.

Assessing schoolchildren's exposure to air pollution during the daily commute - A systematic review

Air pollution is mostly caused by emissions from human activities, and exposure to air pollution is linked with numerous adverse human health outcomes. Recent studies have identified that although people only spend a small proportion of time on their daily commutes, the commuter microenvironment is a significant contributor to their total daily air pollution exposure. Schoolchildren are a particularly vulnerable cohort of the population, and their exposure to air pollution at home or school has been documented in a number of case studies. A few studies have identified that schoolchildren's exposure during commutes is linked with adverse cognitive outcomes and severe wheeze in asthmatic children. However, the determinants of total exposure, such as route choice and commute mode, and their subsequent health impacts on schoolchildren are still not well-understood. The aim of this paper is to review and synthesize recent studies on assessing schoolchildren's exposure to various air pollutants during the daily commute. Through reviewing 31 relevant studies published between 2004 and 2020, we tried to identify consistent patterns, trends, and underlying causal factors in the results. These studies were carried out across 10 commute modes and 12 different air pollutants. Air pollution in cities is highly heterogeneous in time and space, and commuting schoolchildren move through the urban area in complex ways. Measurements from fixed monitoring stations (FMSs), personal monitoring, and air quality modeling are the three most common approaches to determining exposure to ambient air pollutant concentrations. The time-activity diary (TAD), GPS tracker, online route collection app, and GIS-based route simulation are four widely used methods to determine schoolchildren's daily commuting routes. We found that route choices exerted a determining impact on schoolchildren's exposure. It is challenging to rank commute modes in order of exposure, as each scenario has numerous uncontrollable determinants, and there are notable research gaps. We suggest that future studies should concentrate on examining exposure patterns of schoolchildren in developing countries, exposure in the subway and trains, investigating the reliability of current simulation methods, exploring the environmental justice issue, and identifying the health impacts during commuting. It is recommended that three promising tools of smartphones, data fusion, and GIS should be widely used to overcome the challenges encountered in scaling up commuter exposure studies to population scales.

IoT-Enabled Smart Sustainable Cities: Challenges and Approaches

The ongoing diffusion of Internet of Things (IoT) technologies is opening new possibilities, and one of the most remarkable applications is associated with the smart city paradigm, which is continuously evolving. In general, it can be defined as the integration of IoT and Information Communication Technologies (ICT) into city management, with the aim of addressing the exponential growth of urbanization and population, thus significantly increasing people’s quality of life. The smart city paradigm is also strictly connected to sustainability aspects, taking into account, for example, the reduction of environmental impact of urban activities, the optimized management of energy resources, and the design of innovative services and solution for citizens. Abiding by this new paradigm, several cities started a process of strong innovation in different fields (such as mobility and transportation, industry, health, tourism, and education), thanks to significant investments provided by stakeholders and the European Commission (EC). In this paper, we analyze key aspects of an IoT infrastructure for smart cities, outlining the innovations implemented in the city of Parma (Emilia Romagna region, Italy) as a successful example. Special attention is dedicated to the theme of smart urban mobility.

Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations

Spatiotemporal variations in PM_2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM_2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM_2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM_2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM_2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM_2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM_2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM_2.5, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM_2.5, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.

Framework for the Simulation of Sensor Networks Aimed at Evaluating In Situ Calibration Algorithms

The drastically increasing availability of low-cost sensors for environmental monitoring has fostered a large interest in the literature. One particular challenge for such devices is the fast degradation over time of the quality of their data. Therefore, the instruments require frequent calibrations. Traditionally, this operation is carried out on each sensor in dedicated laboratories. This is not economically sustainable for dense networks of low-cost sensors. An alternative that has been investigated is in situ calibration: exploiting the properties of the sensor network, the instruments are calibrated while staying in the field and preferably without any physical intervention. The literature indicates there is wide variety of in situ calibration strategies depending on the type of sensor network deployed. However, there is a lack for a systematic benchmark of calibration algorithms. In this paper, we propose the first framework for the simulation of sensor networks enabling a systematic comparison of in situ calibration strategies with reproducibility, and scalability. We showcase it on a primary test case applied to several calibration strategies for blind and static sensor networks. The performances of calibration are shown to be tightly related to the deployment of the network itself, the parameters of the algorithm and the metrics used to evaluate the results. We study the impact of the main modelling choices and adjustments of parameters in our framework and highlight their influence on the results of the calibration algorithms. We also show how our framework can be used as a tool for the design of a network of low-cost sensors.

Using Gas-Phase Air Quality Sensors to Disentangle Potential Sources in a Los Angeles Neighborhood

In the late summer of 2016, our team deployed a network of low-cost air quality sensing systems in partnership with community-based organizations in a neighborhood in South Los Angeles, California. Residents of this community were concerned about possible emissions from local oil and gas activity, however in addition to these potential emissions, the neighborhood is also subject to a complex mixture of pollutants from other nearby sources including major highways. For this deployment, metal-oxide VOC sensors were quantified to provide methane (CH₄) and total non-methane hydrocarbon (TNMHCs) concentration estimates. This data along with other sensor signals, meteorological data, and community member observations was used to examine the composition and possible origins of observed emissions. The sensor network displayed expected environmental trends and highlighted short-term elevations in CH₄ and/or TNMHCs, which we were then able to investigate more closely. The results indicated that sources of both combusted and volatilized hydrocarbons were likely affecting air quality throughout the community, including near the site of the local oil and gas activity. This deployment may serve as a model for how multi-sensor systems deployed in networks can be leveraged to better understand sources in complex areas, potentially supporting future community-based air quality research.

Applicability of factory calibrated optical particle counters for high-density air quality monitoring networks in Ghana

In this study, we demonstrate the feasibility of using miniaturised optical particle counters (OPCs) for understanding AQ in Sub-Saharan Africa. Specifically, the potential use of OPCs for high-density ground-based air pollution networks and the use of derived data for quantification of atmospheric emissions were investigated. Correlation and trend analysis for particulate matters (PM), including PM₁₀, PM_2.5 and PM₁ were undertaken on hourly basis alongside modelled meteorological parameters. Hourly averaged PM values were 500 μg/m³, 90 μg/m³ and 60 μg/m³ for PM₁₀, PM_2.5 and PM₁, respectively and Pearson's correlation coefficient ranged between 0.97 and 0.98. These levels are in the agreement with range of PM emission reported for these types of environmental settings. PM was locally associated with low wind speeds (<= 2 ms^-1) and was closely linked to anthropogenic activities. This study provides a benchmark for future AQ and demonstrates the feasibility of the current generation of OPCs for AQ monitoring in environments typical of large parts of West and Sub Saharan Africa.

Assessing the usefulness of dense sensor network for PM2.5 monitoring on an academic campus area

Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM_2.5 monitoring on a campus area of Wrocław University of Science and Technology (Wrocław, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM_2.5 concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM_2.5 concentrations. Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM_2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM_2.5 concentrations were even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM_2.5 levels was observed under conditions of temperature inversion.

Performance evaluation of light weight gas sensor system suitable for airborne applications against co-location gas analysers over Delhi

In the present work, we discuss the light-weight gas sensor system (LWGSS) [350 g, 7″ ∗ 3″] originally developed at CSIR-National Physical Laboratory. This instrument is equipped with low-cost electrolytic gas sensors for quantifying major gaseous pollutants present in the atmosphere. Alphasense electrochemical gas sensors were used to measure gas pollutant species such as CO, SO₂, NO₂, O₃ and H₂S. In our experiment, we focus on the observation of CO, SO₂, NO₂, O₃ using this system. LWGSS has been designed for vertical observations using balloons or unmanned aerial vehicles (UAVs) to study the gaseous concentration in the atmospheric boundary layer (ABL). But, before using such instruments in field campaigns, there is a strong need for the inter-comparison of these instruments with that of the collocated high-end gas analysers. Thus, the inter-comparisons were performed between LWGSS and other high-end analysers during 6-7, March 2017 and 26-27, April 2017. The LWGSS system comprising all the sensors was compared against high-end analyser present at CSIR-NPL for ozone and other gas analysers present at IMD, New Delhi. The ozone sensor deployed in LWGSS showed good correlation (i.e. R² = 0.83, slope = 0.93) against the high-end ozone gas analyser, which was calibrated with primary ozone facility (SRP43) available at CSIR-NPL. Inter-comparisons performed for NO₂, SO₂ and CO showed different results. While the NO₂ gas sensor showed medium correlation (R² = 0.75; slope = 0.49), the SO₂ and CO gas sensor showed a poor correlation (and R² = 0.44; slope = 0.98; R² = 0.28, slope = 0.79) respectively, when compared with co-location gas analysers present at IMD, New Delhi. Comparisons were performed for LWGSS data during 1-28 February 2018 with data collected at CPCB station (Shadipur, Delhi) and IMD station (Pusa, Delhi). The comparison results showed variations in LWGSS CO and SO₂ data whereas LWGSS O₃ and NO₂ results were in accordance with data collected at aforementioned monitoring stations.

IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities

Ambient gas detection and measurement had become essential in diverse fields and applications, from preventing accidents, avoiding equipment malfunction, to air pollution warnings and granting the correct gas mixture to patients in hospitals. Gas leakage can reach large proportions, affecting entire neighborhoods or even cities, causing enormous environmental impacts. This paper elaborates on a deep review of the state of the art on gas-sensing technologies, analyzing the opportunities and main characteristics of the transducers, as well as towards their integration through the Internet of Things (IoT) paradigm. This should ease the information collecting and sharing processes, granting better experiences to users, and avoiding major losses and expenses. The most promising wireless-based solutions for ambient gas monitoring are analyzed and discussed, open research topics are identified, and lessons learned are shared to conclude the study.

Research questions to facilitate the future development of European long-term ecosystem research infrastructures: A horizon scanning exercise

Distributed environmental research infrastructures are important to support assessments of the effects of global change on landscapes, ecosystems and society. These infrastructures need to provide continuity to address long-term change, yet be flexible enough to respond to rapid societal and technological developments that modify research priorities. We used a horizon scanning exercise to identify and prioritize emerging research questions for the future development of ecosystem and socio-ecological research infrastructures in Europe. Twenty research questions covered topics related to (i) ecosystem structures and processes, (ii) the impacts of anthropogenic drivers on ecosystems, (iii) ecosystem services and socio-ecological systems and (iv), methods and research infrastructures. Several key priorities for the development of research infrastructures emerged. Addressing complex environmental issues requires the adoption of a whole-system approach, achieved through integration of biotic, abiotic and socio-economic measurements. Interoperability among different research infrastructures needs to be improved by developing standard measurements, harmonizing methods, and establishing capacities and tools for data integration, processing, storage and analysis. Future research infrastructures should support a range of methodological approaches including observation, experiments and modelling. They should also have flexibility to respond to new requirements, for example by adjusting the spatio-temporal design of measurements. When new methods are introduced, compatibility with important long-term data series must be ensured. Finally, indicators, tools, and transdisciplinary approaches to identify, quantify and value ecosystem services across spatial scales and domains need to be advanced.

A site-optimised multi-scale GIS based land use regression model for simulating local scale patterns in air pollution

Standard Land Use Regression (LUR) models rely on one universal equation for the entire city or study area. Since this approach cannot represent the heterogeneous controls on pollutant dispersion in central, urban and suburban areas effectively the models are not transferable. Further, if different land use types are not adequately sampled in the measurement campaign, model estimates of local-scale pollutant concentrations may be poor. In this study, this deficiency is overcome with a site-optimised multi-scale GIS based LUR modelling approach developed. This approach is used to simulate nitrogen dioxide (NO₂) concentrations in Auckland at three scales (central business district (CBD), urban, and suburban). The simulated NO₂ distribution clearly shows a higher concentration of pollution along arterial roads and motorways as expected. Areas of limited dispersion (such as among high-rise buildings of the CBD) are also identified as high pollution areas. Predictor variables vary between scales; no single variable is common to all the scales. The leave-one-out cross validation (LOOCV) revealed that the multi-scale LUR model achieved an R² of 0.62, 0.86 and 0.73, respectively, at the CBD, urban, and suburban scales. The corresponding LOOCV root-mean-square-errors (RMSE) were 5.58, 3.53 and 4.41 μg·m^-3 respectively. Based on these statistical measures the multi-scale LUR model performs slightly better than the universal kriging (UK) model and the standard LUR model, and significantly better than the inverse distance weighting (IDW) and ordinary kriging (OK) models. When evaluated against external observations at eight fixed regulatory monitoring stations, the multi-scale LUR model out-performed all four of the other models considered and achieved an R² value of 0.85 with the lowest RMSE (8.48 μg·m^-3). This approach offers a robust alternative for modelling and mapping spatial concentrations of NO₂ pollutants at multi-scales in large study areas with distinct urban design and configurations.

Development and Implementation of a Platform for Public Information on Air Quality, Sensor Measurements, and Citizen Science

The use of low-cost sensors for air quality measurements is expanding rapidly, with an associated rise in the number of citizens measuring air quality themselves. This has major implications for traditional air quality monitoring as performed by Environmental Protection Agencies. Here we reflect on the experiences of the Dutch Institute for Public Health and the Environment (RIVM) with the use of low-cost sensors, particularly NO2 and PM10/PM2.5-sensors, and related citizen science, over the last few years. Specifically, we discuss the Dutch Innovation Program for Environmental Monitoring, which comprises the development of a knowledge portal and sensor data portal, new calibration approaches for sensors, and modelling and assimilation techniques for incorporating these uncertain sensor data into air pollution models. Finally, we highlight some of the challenges that come with the use of low-cost sensors for air quality monitoring, and give some specific use-case examples. Our results show that low-cost sensors can be a valuable addition to traditional air quality monitoring, but so far, their use in official monitoring has been limited. More research is needed to establish robust calibration methods while ongoing work is also aimed at a better understanding of the public’s needs for air quality information to optimize the use of low-cost sensors.

Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice

Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM_2.5 on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO₂ and SO₂) were used to differentiate between traffic (higher NO₂ concentrations) and industrial (higher SO₂ concentrations) sources of PM_2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM_2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM_2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM_2.5 or NO₂ concentration. The analysis conducted here highlights differences in PM_2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.

Vertical monitoring of traffic-related air pollution (TRAP) in urban street canyons of Hong Kong

Rapid urbanization has significantly increased air pollution especially in urban regions with high traffic volumes. Existing methods for estimating traffic-related air pollution (TRAP) and TRAP-related health impacts are based on two-dimensional modelling. This paper describes a point-based methodology to monitor vertical pollutant concentrations in typical street canyons of Hong Kong. It explains the conceptual design, monitoring strategy and selection criteria for a limited number of receptor locations in street canyons to undertake field measurements for both outdoor exposure and indoor infiltration. It also expounds on the limitations and complications associated with field instrumentation and retention of participating home units. The empirical results were applied on the building infiltration efficiencies assessment. It is concluded that the cost-effective field methodology developed in this paper expects to strike a balance between exposure error and limited data locations. These findings will have important implications in future monitoring design of vertical TRAP exposure to support health studies.

Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility

Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels ± standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 ± 0.2 mg m-3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m-3 for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks.

An Exposure Appraisal of Outdoor Air Pollution on the Respiratory Well-being of a Developing City Population

Zaria is the educational hub of northern Nigeria. It is a developing city with a pollution level high enough to be ranked amongst the World Health Organization’s (WHO) most polluted cities. The study appraised the influence of outdoor air pollution on the respiratory well-being of a population in a limited resource environment. With the approved ethics, the techniques utilized were: portable pollutant monitors, respiratory health records, WHO AirQ+ software, and the American Thoracic Society (ATS) questionnaire. They were utilized to acquire day-time weighted outdoor pollution levels, health respiratory cases, assumed baseline incidence (BI), and exposure respiratory symptoms among selected study participants respectively. The study revealed an average respiratory illness incidence rate of 607 per 100,000 cases. Findings showed that an average of 2648 cases could have been avoided if the theoretical WHO threshold limit for the particulate matter with diameter of <2.5/10 micron (PM_2.5/PM₁₀) were adhered to. Using the questionnaire survey, phlegm was identified as the predominant respiratory symptom. A regression analysis showed that the criteria pollutant PM_2.5, was the most predominant cause of respiratory symptoms among interviewed respondents. The study logistics revealed that outdoor pollution is significantly associated with respiratory well-being of the study population in Zaria, Nigeria.

Testing the performance of sensors for ozone pollution monitoring in a citizen science approach

Tropospheric ozone (O₃) is an environmental pollutant of growing concern, especially in suburban and rural areas where the density of air quality monitoring stations is not high. In this type of areas citizen science strategies can be useful tools for awareness raising, but sensor technologies must be validated before sensor data are communicated to the public. In this work, the performance under field conditions of two custom-made types of ozone sensing devices, based on metal-oxide and electrochemical sensors, was tested. A large array of 132 metal-oxide (Sensortech MICS 2614) and 11 electrochemical (Alphasense) ozone sensors, built into 44 sensing devices, was co-located at reference stations in Italy (4 stations) and Spain (5). Mean R² between sensor and reference data was 0.88 (0.78-0.96) and 0.89 (0.73-0.96) for Captor (metal-oxide) and Raptor (electrochemical) nodes. The metal-oxide sensors showed an upper limit (approximately 170 μg/m³) implying that these sensors may be useful to communicate mean ozone concentrations but not peak episodes. The uncertainty of the nodes was 10% between 100 and 150 μg/m³ and 20% between 150 and 200 μg/m³, for Captors, and 10% for >100 μg/m³ for Raptors. Operating both types of nodes up to 5 months did not evidence any clear influence of drifts. The use of these sensors in citizen science can be a useful tool for awareness raising. However, significant data processing efforts are required to ensure high data quality, and thus machine learning strategies are advisable. Relative uncertainties should always be reported when communicating ozone concentration data from sensing nodes.

Developing air exchange rate models by evaluating vehicle in-cabin air pollutant exposures in a highway and tunnel setting: case study of Tehran, Iran

The passengers inside vehicles could be exposed to high levels of air pollutants particularly while driving on highly polluted and congested traffic roadways. In order to study such exposure levels and its relation to the cabin ventilation condition, a monitoring campaign was conducted to measure the levels inside the three most common types of vehicles in Tehran, Iran (a highly air polluted megacity). In this regard, carbon monoxide (CO) and particulate matter (PM) were measured for various ventilation settings, window positions, and vehicle speeds while driving on the Resalat Highway and through the Resalat Tunnel. Results showed on average in-cabin exposure to particle number and PM₁₀ for the open windows condition was seven times greater when compared to closed windows and air conditioning on. When the vehicle was passing through the tunnel, in-cabin CO and particle number increased 100 and 30%, respectively, compared to driving on highway. Air exchange rate (AER) is a significant factor when evaluating in-cabin air pollutants level. AER was measured and simulated by a model developed through a Monte Carlo analysis of uncertainty and considering two main affecting variables, vehicle speed and fan speed. The lowest AER was 7 h^-1 for the closed window and AC on conditions, whereas the highest AER was measured 70 h^-1 for an open window condition and speed of 90 km h^-1. The results of our study can assist policy makers in controlling in-cabin pollutant exposure and in planning effective strategies for the protection of public health.

Wireless Sensor Networks for Long-Term Monitoring of Urban Noise

Noise pollution in urban environments is becoming increasingly common and it has potential to negatively impact people's health and decrease overall productivity. In order to alleviate these effects, it is important to better quantify noise patterns and levels through data collection and analysis. Wireless sensor networks offer a method for achieving this with a higher level of granularity than traditional handheld devices. In this study, a wireless sensing unit (WSU) was developed that possesses the same functionality as a handheld sound level meter. The WSU is comprised of a microcontroller unit that enables on-board computations, a wireless transceiver that uses Zigbee protocol for data transmission, and an external peripheral board that houses the microphone transducer. The WSU utilizes on-board data processing techniques to monitor noise by computing equivalent continuous sound levels, LeqT, which effectively minimizes data transmission and increases the overall longevity of the node. Strategies are also employed to ensure real-time functionality is maintained on the sensing unit, with a focus on preventing bottlenecks between data acquisition, data processing, and wireless transmission. Four units were deployed in two weeks field validation test and were shown to be capable of monitoring noise for extended periods of time.

Towards an integrated framework for air quality monitoring and exposure estimation-a review

For the health and safety of the public, it is essential to measure spatiotemporal distribution of air pollution in a region and thus monitor air quality in a fine-grain manner. While most of the sensing-based commercial applications available until today have been using fixed environmental sensors, the use of personal devices such as smartphones, smartwatches, and other wearable devices has not been explored in depth. These kinds of devices have an advantage of being with the user continuously, thus providing an ability to generate accurate and well-distributed spatiotemporal air pollution data. In this paper, we review the studies (especially in the last decade) done by various researchers using different kinds of environmental sensors highlighting related techniques and issues. We also present important studies of measuring impact and emission of air pollution on human beings and also discuss models using which air pollution inhalation can be associated to humans by quantifying personal exposure with the use of human activity detection. The overarching aim of this review is to provide novel and key ideas that have the potential to drive pervasive and individual centric and yet accurate pollution monitoring techniques which can scale up to the future needs.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a one-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area - highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.