Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors

Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

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.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

BACKGROUND

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment.

OBJECTIVE

We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces.

METHODS

We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model.

RESULTS

The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results.

SIGNIFICANCE

A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts.

IMPACT STATEMENT

Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform

This paper describes a mobile air pollution sampling system, the Urban Scanner, which aims at gathering dense spatiotemporal air quality data to support urban air quality and exposure science. Urban Scanner comprises custom vehicle-mounted sensors for air pollution, meteorology, and built environment data collection (low-cost sensors, wind anemometer, 360 deg camera, LIDAR, GPS) as well as a server to store, process, and map all gathered geo-referenced sensory information. Two levels of sensor calibration were implemented, both in a chamber and in the field, against reference instrumentation. Chamber tests and a set of mathematical tools were developed to correct for sensor noise (wavelet denoising), misalignment (linear and nonlinear), and hysteresis removal. Models based on chamber testing were further refined based on field co-location. While field co-location captures natural changes in air pollution and meteorology, chamber tests allow for simulating fast transitions in these variables, like the transitions experienced by a mobile sensor in an urban environment. The best suite of models achieved an R² higher than 0.9 between sensor output and reference station observations and an RMSE of 2.88 ppb for nitrogen dioxide and 4.03 ppb for ozone. A mobile sampling campaign was conducted in the city of Toronto, Canada, to further test Urban Scanner. We observe that the platform adequately captures spatial and temporal variability in urban air pollution, leading to the development of land-use regression models with high explanatory power.

Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO₂), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO₂. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements

Within-city ultrafine particle (UFP) concentrations vary sharply since they are influenced by various factors. We developed prediction models for short-term UFP exposures using street-level images collected by a camera installed on a vehicle rooftop, paired with air quality measurements conducted during a large-scale mobile monitoring campaign in Toronto, Canada. Convolutional neural network models were trained to extract traffic and built environment features from images. These features, along with regional air quality and meteorology data were used to predict short-term UFP concentration as a continuous and categorical variable. A gradient boost model for UFP as a continuous variable achieved R² = 0.66 and RMSE = 9391.8#/cm³ (mean values for 10-fold cross-validation). The model predicting categorical UFP achieved accuracies for "Low" and "High" UFP of 77 and 70%, respectively. The presence of trucks and other traffic parameters were associated with higher UFPs, and the spatial distribution of elevated short-term UFP followed the distribution of single-unit trucks. This study demonstrates that pictures captured on urban streets, associated with regional air quality and meteorology, can adequately predict short-term UFP exposure. Capturing the spatial distribution of high-frequency short-term UFP spikes in urban areas provides crucial information for the management of near-road air pollution hot spots.

Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data

Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-m interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R² and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.

Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models

BACKGROUND

Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians' exposure to particulate matter (black carbon (BC) and PM_2.5 mass concentrations).

OBJECTIVE

We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure.

METHODS

We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error.

RESULTS

The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM_2.5.

SIGNIFICANCE

Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM_2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM_2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM_2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM_2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM_2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R² of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R² of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM_2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM₁) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM₁ based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM₁. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m³; PM₁: 56.97 μg/m³) than in fall (BC: 3.74 μg/m³; PM₁: 33.29 μg/m³) and summer (BC: 2.77 μg/m³; PM₁: 27.04 μg/m³). For both BC and PM₁, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R² of the LUR models for BC and PM₁ ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM₁ models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM₁ was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM₁ in typical inner-city environments, and support pollution management.

Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring

BACKGROUND

Large nation- and region-wide epidemiological studies have provided important insights into the health effects of long-term exposure to outdoor air pollution. Evidence from these studies for the long-term effects of ultrafine particles (UFP), however is lacking. Reason for this is the shortage of empirical UFP land use regression models spanning large geographical areas including cities with varying topographies, peri-urban and rural areas. The aim of this paper is to combine targeted mobile monitoring and long-term regional background monitoring to develop national UFP models.

METHOD

We used an electric car to monitor UFP concentrations in selected cities and towns across the Netherlands over a 14-month period in 2016-2017. Routes were monitored 3 times and concentrations were averaged per road segment. In addition, we used kriging maps based on regional background monitoring (20 sites; 3 × 2 weeks) over the same period to assess annual average regional background concentrations. All road segments were used to model spatial variation of UFP with three different land-use (regression) approaches: supervised stepwise regression, LASSO and random forest. For each approach, we also tested a deconvolution method, which segregates the average concentration at each road segment into a local and background signal. Model performance was evaluated with short-term (400 sites across the Netherlands; 3 × 30 minutes) and external longer-term measurements (42 sites in two major cities; 3 × 24 hours). We also compared predictions of all six models at 1000 random addresses spread over the country.

RESULTS

We found similar predictive performance for the six models, with validation R² values from 0.25 to 0.35 for short-term measurements and 0.52 to 0.60 for longer-term external measurements. Models with and without deconvolution had similar predictive performance. All models based on the deconvolution method included a regional background kriging map as important predictor. Correlations between predictions at random addresses were high with Pearson correlations from 0.84 to 0.99. Models overestimated exposure at the short-term and long-term sites by about 20-30% in all cases, with small differences between regions and road types.

CONCLUSION

We developed robust nation-wide models for long-term UFP exposure combining mobile monitoring with long-term regional background monitoring. Minor differences in predictive performance between different algorithms were found, but the deconvolution approach is considered more physically realistic. The models will be applied in Dutch nation-wide health studies.

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models

Previous studies have characterized spatial patterns of pollution with land use regression (LUR) models from distributed passive or filter samplers at low temporal resolution. Large-scale deployment of low-cost sensors (LCS), which typically sample in real time, may enable time-resolved or real-time modeling of concentration surfaces. The aim of this study was to develop spatiotemporal models of PM_2.5, NO₂, and CO using an LCS network in Pittsburgh, Pennsylvania. We modeled daily average concentrations in August 2016-December 2017 across 50 sites. Land use variables included 13 time-independent (e.g., elevation) and time-dependent (e.g., temperature) predictors. We examined two models: LUR and a machine-learning-enabled land use model (land use random forest, LURF). The LURF models outperformed LUR models, with increase in the average externally cross-validated R² of 0.10-0.19. Using wavelet decomposition to separate short-lived events from the regional background, we also created time-decomposed LUR and LURF models. Compared to the standard model, this resulted in improvement in R² of up to 0.14. The time-decomposed models were more influenced by spatial parameters. Mapping our models across Allegheny County, we observed that time-decomposed LURF models created robust PM_2.5 predictions, suggesting that this approach may improve our ability to map air pollutants at high spatiotemporal resolution.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53-0.87 (PM_2.5), 0.53-0.85 (PM₁₀), and 0.33-0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM_2.5 and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM_2.5/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM_2.5 and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Evaluation of air quality in Sunway City, Selangor, Malaysia from a mobile monitoring campaign using air pollution micro-sensors

Due to the increase of the human population and the rapid industrial growth in the past few decades, air quality monitoring is essential to assess the pollutant levels of an area. However, monitoring air quality in a high-density area like Sunway City, Selangor, Malaysia is challenging due to the limitation of the local monitoring network. To establish a comprehensive data for air pollution in Sunway City, a mobile monitoring campaign was employed around the city area with a duration of approximately 6 months, from September 2018 to March 2019. Measurements of air pollutants such as carbon dioxide (CO₂) and nitrogen dioxide (NO₂) were performed by using mobile air pollution sensors facilitated with a GPS device. In order to acquire a more in-depth understanding on traffic-related air pollution, the measurement period was divided into two different time blocks, which were morning hours (8 a.m.-12 p.m.) and afternoon hours (3 p.m.-7 p.m.). The data set was analysed by splitting Sunway City into different zones and routes to differentiate the conditions of each region. Meteorological variables such as ambient temperature, relative humidity, and wind speed were studied in line with the pollutant concentrations. The air quality in Sunway City was then compared with various air quality standards such as Malaysian Air Quality Standards and World Health Organisation (WHO) guidelines to understand the risk of exposure to air pollution by the residence in Sunway City.

Predicting Traffic-Related Air Pollution Using Feature Extraction from Built Environment Images

This study develops a set of algorithms to extract built environment features from Google aerial and street view images, reflecting the microcharacteristics of an urban location as well as the different functions of buildings. These features were used to train a Bayesian regularized artificial neural network (BRANN) model to predict near-road air quality based on measurements of ultrafine particles (UFPs) and black carbon (BC) in Toronto, Canada. The resulting models [adjusted R² of 75.87 and 79.10% for UFP and BC and root mean squared error (RMSE) of 21,800 part/cm³ and 1300 ng/m³ for UFP and BC] were compared with similar ANN models developed using the same predictors, but extracted from traditional geographic information system (GIS) databases [adjusted R² of 58.74 and 64.21% for UFP and BC and RMSE values of 23,000 part/cm³ and 1600 ng/m³ for UFP and BC]. The models based on feature extraction exhibited higher predictive power, thus highlighting the greater accuracy of the proposed methods compared to GIS layers that are solely based on aerial images. A comparison with other neural network approaches as well as with a traditional land-use regression model demonstrates the strength of the BRANN model for spatial interpolation of air quality.

Modelling Cyclists’ Multi-Exposure to Air and Noise Pollution with Low-Cost Sensors—The Case of Paris

Cyclists are particularly exposed to air and noise pollution because of their higher ventilation rate and their proximity to traffic. However, few studies have investigated their multi-exposure and have taken into account its real complexity in building statistical models (nonlinearity, pseudo replication, autocorrelation, etc.). We propose here to model cyclists’ exposure to air and noise pollution simultaneously in Paris (France). Specifically, the purpose of this study is to develop a methodology based on an extensive mobile data collection using low-cost sensors to determine which factors of the urban micro-scale environment contribute to cyclists’ multi-exposure and to what extent. To this end, we developed a conceptual framework to define cyclists’ multi-exposure and applied it to a multivariate generalized additive model with mixed effects and temporal autocorrelation. The results show that it is possible to reduce cyclists’ multi-exposure by adapting the planning and development practices of cycling infrastructure, and that this reduction can be substantial for noise exposure.

Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait

Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize the quality of the indoor air environment of the Co-Ops supermarkets in Kuwait based on investigation of CO2, CO, NO2, H2S, TVOCs, and NMHC. On-site measurements were conducted to evaluate these parameters in three locations at the selected Co-Ops, and the perceived air quality (PAQ) was determined to quantify the air’s pollutants as perceived by humans. Moreover, the indoor air quality index (AQI) was constructed for the selected locations, and the ANOVA test was used to analyze the association between the observed concentrations among these environmental parameters. At least in one spot at each Co-Op, the tested environmental parameters exceeded the threshold limit set by the environmental agencies. The PAQ for Co-Op1, 2, and 3 are 1.25, 1.00, and 0.75 respectively. CO2 was significantly found in an association with CO, H2S, and TVOCs, and its indoor-outdoor concentrations were significantly correlated with R2 values ranges from 0.40 to 0.86 depending on the tested location.

Land use regression for spatial distribution of urban particulate matter (PM10) and sulfur dioxide (SO2) in a heavily polluted city in Northeast China

Particulate material 10 μm (PM₁₀) and sulfur dioxide (SO₂) are representative air pollutants in Northeast China and may contribute more to the morbidity of respiratory and cardiovascular disease than may other pollutants. Up to now, there have been few studies on the relation between health effect and air pollution by PM₁₀ and SO₂ in Northeast China, which may be due to the lack of a model for determination of air pollution exposure. For the first time, we used daily concentration data and influencing factors (different type of land use, road length and population density, and weather conditions as well) to develop land use regression models for spatial distribution of PM₁₀ and SO₂ in a central city in Northeast China in both heating and non-heating months. The final models of SO₂ and PM₁₀ estimation showed good performance (heating months: R² = 0.88 for SO₂, R² = 0.88 for PM₁₀; non-heating months: R² = 0.79 for SO₂; R² = 0.87 for PM₁₀). Estimated concentrations of air pollutants were more affected by population density in heating seasons and land use area in non-heating seasons. We used the land use regression (LUR) models developed to predict pollutant levels in nine districts in Shenyang and conducted a correlation analysis between air pollutant levels and hospital admission rates for childhood asthma. There were high associations between asthma hospital admission rates and air pollution levels of SO₂ and PM₁₀, which indicated the usability of the LUR models and the need for more concern about the health effects of SO₂ and PM₁₀ in Northeast China. This study may contribute to epidemiological research on the relation between air pollutant exposure and typical chronic disease in Northeast China as well as providing the government with more scientific recommendations for air pollution prevention.

Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

This study presents land-use regression (LUR) models for submicron particulate matter (PM₁) components from an urban area. Models are presented for mass concentrations of inorganic species (SO₄, NO₃, NH₄), organic aerosol (OA) factors, and total PM₁. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km² area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R² = 0.80, HOA R² = 0.67) and lowest for secondary inorganic species (SO₄ R² = 0.47, NH₄ R² = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM₁ components through a subsampling analysis.

Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities

Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R²), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R²; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R² ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.

Fine-scale spatial structure of air pollutant concentrations along bus routes

Air pollution measurements were made at 5 s intervals along a bus route in Hong Kong, chosen to avoid frequent intersections or traffic jams. This allowed periodic pollutant concentration patterns in bus-derived pollutants to be explored using Fourier analysis. The analysis showed the defining role of the bus stops in terms of NO_X and PM_2.5 concentrations, which have profiles with a sawtooth form. Early in inter-stop segments, concentrations are high, followed by a decline. This arises from high emissions during the acceleration away from the bus stop, then lower emissions during cruise and de-acceleration. This pattern can be enhanced on sections of the route where buses are frequent as pollutants accumulate from the larger number of buses. The high concentrations just beyond bus stops may have implications for planning bus routes and the positioning of bus stops.

Spatial characteristics and determinants of in-traffic black carbon in Shanghai, China: Combination of mobile monitoring and land use regression model

Black carbon (BC) has emerged as a major contributor to global climate change. Cities play an important role in global BC emission. The present study investigated the spatial pattern of in-traffic BC at a high spatial resolution in Shanghai, the commercial and financial center in Mainland China. The determinants including road network, social economic status and point-source pollutants, which may influence the BC spatial variability were also discussed. From October to December 2016, mobile monitoring was conducted to assess the BC concentrations on three sampling routes in Shanghai with a total length of 116 km. The results showed that the mean in-traffic BC among three sampling routes was 10.77 ± 3.50 μg/m³. BC concentrations showed a significant spatial heterogeneity. The highest BC concentrations were near industrial sources and that those high concentrations were associated with either direct emissions from the industries, freight traffic, or both. With the widely distributed polluting enterprises and high emitting vehicles, the average BC in the low urbanization areas (12.80 ± 4.54 μg/m³) was 57% higher than that in the urban core (7.77 ± 2.24 μg/m³). Furthermore, a land use regression (LUR) model based on mobile monitoring was developed to examine the determinants and its spatial variability of BC measurements which corresponded to 17 predictor variables, e.g. road network, land use, meteorological condition etc., in 7 buffer distances (100 m to 10 km). The variables of meteorological, socio-economical and the distance to BC point-sources were selected as the independent variables. It was found that the established LUR model could explain a proportion (68%) of the variability of BC. LUR modeling from mobile measurements was possible, but more work related to the effect of traffic regulation on BC could be helpful for informing best model practice.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R² for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R² than the LUR-K approach within 4 to 8 repeated drive days per road segment.