National Land Use Regression Model for NO2 Using Street View Imagery and Satellite Observations

High-Resolution Geospatial Database: National Criteria-Air-Pollutant Concentrations in the Contiguous U.S., 2016-2020

Concentration estimates for ambient air pollution are used widely in fields such as environmental epidemiology, health impact assessment, urban planning, environmental equity and sustainability. This study builds on previous efforts by developing an updated high-resolution geospatial database of population-weighted annual-average concentrations for six criteria air pollutants (PM2.5, PM10, CO, NO2, SO2, O3) across the contiguous U.S. during a five-year period (2016-2020). We developed Land Use Regression (LUR) models within a partial-least-squares-universal kriging framework by incorporating several land use, geospatial and satellite-based predictor variables. The LUR models were validated using conventional and clustered cross-validation, with the former consistently showing superior performance in capturing the variability of air quality. Most models demonstrated reliable performance (e.g., mean squared error-based R 2 > 0.8, standardised root mean squared error < 0.1). We used the best modelling approach to develop estimates by Census Block, which were then population-weighted averaged at Census Block Group, Census Tract and County geographies. Our database provides valuable insights into the dynamics of air pollution, with utility for environmental risk assessment, public health, policy and urban planning.

Development and evaluation of rapid, national-scale outdoor air pollution modelling and exposure assessment: Hybrid Air Dispersion Exposure System (HADES)

Improvements in computer processing power are facilitating the development of more detailed environmental models with greater geographical coverage. We developed a national-scale model of outdoor air pollution (Hybrid Air Dispersion Exposure System - HADES) for rapid production of concentration maps of nitrogen dioxide (NO2) and ozone (O3) at very high spatial resolution (10m). The model combines dispersion modelling with satellite-derived estimates of background concentrations, land cover, and a 3-D representation of buildings, in a statistical calibration framework. We developed an emissions inventory covering England and Wales to implement the model and tested its performance using concentration data for the years 2018-2019 from fixed-site monitoring locations. In 10,000 Monte Carlo cross-validation iterations, hourly-annual average R2 values for NO2 were 0.77-0.79 (RMSE: root mean squared error of 5.3-5.7 µg/m3), and 0.87-0.89 for O3 (RMSE = 3.6-3.8 µg/m3) at the 95% confidence interval. The annual average R2 was 0.80 for NO2 (RMSE = 4.9 µg/m3) and 0.86 for O3 (RMSE = 3.2 µg/m3) from aggregating the hourly-annual estimates. The air pollution surfaces are freely available for non-commercial use. In using these surfaces for exposure assessment, all residential locations, and neighbourhoods in urban areas, are unlikely to be below the 2021 World Health Organisation Air Quality Guidelines threshold (10 µg/m3) for annual average NO2 concentrations (10 µg/m3). Rural and suburban areas are likely to exceed the peak-season 8-hour daily maximum O3 threshold (60 µg/m3).

Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy

Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO2, PM2.5, and PM10, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m). Additionally, three typical machine learning algorithms were compared with SVI-based land-used regression (LUR) model to explore their performances. Generally, machine learning methods outperform linear LUR, with the ranking: random forest > XGBoost > neural network > LUR. Averaging strategy is an effective method to avoid bias of insufficient feature capture. Therefore, the optimal sampling strategy is to integrating multiple viewing angles at a 100-m buffer, which achieved absolute errors mostly less than 2.5 μg/m3 or ppb. Besides, overexposure, blur, and underexposure led to image misjudgments and incorrect identifications, causing an overestimation of road features and underestimation of human-activity features. These findings enhance understanding and offer valuable support for developing image-based air quality models and other SVI-related research.

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants

Urban air quality persists as a global concern, with critical health implications. This study employs a combination of machine learning (gradient boosting regression, GBR) and spatial analysis to better understand the key drivers behind air pollution and its prediction and mitigation strategies. Focusing on New York City as a representative urban area, we investigate the interplay between urban characteristics and weather factors, showing that urban features, including traffic-related parameters and urban morphology, emerge as crucial predictors for pollutants closely associated with vehicular emissions, such as elemental carbon (EC) and nitrogen oxides (NOx). Conversely, pollutants with secondary formation pathways (e.g., PM2.5) or stemming from nontraffic sources (e.g., sulfur dioxide, SO2) are predominantly influenced by meteorological conditions, particularly wind speed and maximum daily temperature. Urban characteristics are shown to act over spatial scales of 500 × 500 m2, which is thus the footprint needed to effectively capture the impact of urban form, fabric, and function. Our spatial predictive model, needing only meteorological and urban inputs, achieves promising results with mean absolute errors ranging from 8 to 32% when using full-year data. Our approach also yields good performance when applied to the temporal mapping of spatial pollutant variability. Our findings highlight the interacting roles of urban characteristics and weather conditions and can inform urban planning, design, and policy.

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.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Apparent quantum yield for photo-production of singlet oxygen in reservoirs and its relation to the water matrix

Man-made reservoirs are important for human daily lives and offer different functions, however they are contaminated due to anthropogenic activities. Dissolved organic matter (DOM) from each reservoir is unique in composition, which further determines its photo-reactivity. Thus, this study aimed to investigate the photo-reactivity of reservoir DOM in terms of the quantum yield for photo-production of singlet oxygen (Ф1O2). We sampled surface water of 50 reservoirs in Japan and determined their Ф1O2 using simulated sunlight together with bulk water analysis. Their Ф1O2 ranged from 1.46 × 10-2 to 6.21 × 10-2 (mean, 2.55 × 10-2), which was identical to those of lakes and rivers reported in the literature, but lower than those of wetland water and wastewater. High-energy triplet-state of DOM accounted for 59.4% of the 1O2 production in the reservoir water on average. Among the bulk water properties, the spectral slope of wavelength from 350 to 400 nm (S350-400) was statistically detected as the most important predictor for Ф1O2. Furthermore, the multiple linear regression model employed S350-400 and the biological index as predictors with no intercorrelations and reasonable accuracy (r2 = 0.86), while the random forest model showed a better accuracy (r2 = 0.90). Overall, these major findings are beneficial for understanding the photo-reactivity of reservoir waters.

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models

BACKGROUND

Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

OBJECTIVE

This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto.

METHODS

We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models.

RESULTS

In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses.

CONCLUSION

Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.

Spatial Differentiation of PM2.5 Concentration and Analysis of Atmospheric Health Patterns in the Xiamen-Zhangzhou-QuanZhou Urban Agglomeration

Exploring the spatial differentiation of PM2.5 concentrations in typical urban agglomerations and analyzing their atmospheric health patterns are necessary for building high-quality urban agglomerations. Taking the Xiamen-Zhangzhou-Quanzhou urban agglomeration as an example, and based on exploratory data analysis and mathematical statistics, we explore the PM2.5 spatial distribution patterns and characteristics and use hierarchical analysis to construct an atmospheric health evaluation system consisting of exposure-response degree, regional vulnerability, and regional adaptation, and then identify the spatial differentiation characteristics and critical causes of the atmospheric health pattern. This study shows the following: (1) The average annual PM2.5 value of the area in 2020 was 19.16 μg/m3, which was lower than China's mean annual quality concentration limit, and the overall performance was clean. (2) The spatial distribution patterns of the components of the atmospheric health evaluation system are different, with the overall cleanliness benefit showing a "north-central-south depression, the rest of the region is mixed," the regional vulnerability showing a coastal to inland decay, and the regional adaptability showing a "high north, low south, high east, low west" spatial divergence pattern. (3) The high-value area of the air health pattern of the area is an "F-shaped" spatial distribution; the low-value area shows a pattern of "north-middle-south" peaks standing side by side. The assessment of health patterns in the aforementioned areas can provide theoretical references for pollution prevention and control and the construction of healthy cities.

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.