Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images

Airborne Nanoparticle Concentrations Are Associated with Increased Mortality Risk in Canada's Two Largest Cities

Rationale: Outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter ⩽2.5 μm; PM2.5) contributes to millions of deaths around the world each year, but much less is known about the long-term health impacts of other particulate air pollutants, including ultrafine particles (a.k.a. nanoparticles), which are in the nanometer-size range (<100 nm), widespread in urban environments, and not currently regulated. Objectives: We sought to estimate the associations between long-term exposure to outdoor ultrafine particles and mortality. Methods: Outdoor air pollution levels were linked to the residential addresses of a large, population-based cohort from 2001 to 2016. Associations between long-term exposure to outdoor ultrafine particles and nonaccidental and cause-specific mortality were estimated using Cox proportional hazards models. Measurements and Main Results: An increase in long-term exposure to outdoor ultrafine particles was associated with an increased risk of nonaccidental mortality (hazard ratio = 1.073; 95% confidence interval = 1.061-1.085) and cause-specific mortality, the strongest of which was respiratory mortality (hazard ratio = 1.174; 95% confidence interval = 1.130-1.220). We estimated the mortality burden for outdoor ultrafine particles in Montreal and Toronto, Canada, to be approximately 1,100 additional nonaccidental deaths every year. Furthermore, we observed possible confounding by particle size, which suggests that previous studies may have underestimated or missed important health risks associated with ultrafine particles. Conclusions: As outdoor ultrafine particles are not currently regulated, there is great potential for future regulatory interventions to improve population health by targeting these common outdoor air pollutants.

Spatial and spatiotemporal modelling of intra-urban ultrafine particles: A comparison of linear, nonlinear, regularized, and machine learning methods

BACKGROUND

Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs.

OBJECTIVE

To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs.

METHODS

Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation.

RESULTS

Mean annual UFP concentrations was 13,335 particles/cm3. Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R2 = 0.78-0.86, RMSE = 2163-2169 particles/cm3; daily R2 = 0.47-0.48, RMSE = 8651-11,422 particles/cm3). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models.

CONCLUSIONS

The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies.

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominantly focus on local weather conditions, overlooking the influences from high-altitude and broader regional meteorological patterns. Here, we employ convolutional neural networks (CNNs), a technique typically applied to image recognition, to investigate the influence of three-dimensional spatial variations in meteorological fields on the daily, seasonal, and interannual dynamics of ozone in Shenzhen, a major coastal urban center in China. Our optimized CNNs model, covering a 13° × 13° spatial domain, effectively explains over 70% of daily ozone variability, outperforming alternative empirical approaches by 7 to 62%. Model interpretations reveal the crucial roles of 2-m temperature and humidity as primary drivers, contributing 16% and 15% to daily ozone fluctuations, respectively. Regional wind fields account for up to 40% of ozone changes during the episodes. CNNs successfully replicate observed ozone temporal patterns, attributing -5-6 μg·m-3 of interannual ozone variability to weather anomalies. Our interpretable CNNs framework enables quantitative attribution of historical ozone fluctuations to nonlinear meteorological effects across spatiotemporal scales, offering vital process-based insights for managing megacity air quality amidst changing climate regimes.

Predicting within-city spatiotemporal variations in daily median outdoor ultrafine particle number concentrations and size in Montreal and Toronto, Canada

Background

Epidemiological evidence suggests that long-term exposure to outdoor ultrafine particles (UFPs, <0.1 μm) may have important human health impacts. However, less is known about the acute health impacts of these pollutants as few models are available to estimate daily within-city spatiotemporal variations in outdoor UFPs.

Methods

Several machine learning approaches (i.e., generalized additive models, random forest models, and extreme gradient boosting) were used to predict daily spatiotemporal variations in outdoor UFPs (number concentration and size) across Montreal and Toronto, Canada using a large database of mobile monitoring measurements. Separate models were developed for each city and all models were evaluated using a 10-fold cross-validation procedure.

Results

In total, our models were based on measurements from 12,705 road segments in Montreal and 10,929 road segments in Toronto. Daily median outdoor UFP number concentrations varied substantially across both cities with 1st-99th percentiles ranging from 1389 to 181,672 in Montreal and 2472 to 118,544 in Toronto. Outdoor UFP size tended to be smaller in Montreal (mean [SD]: 34 nm [15]) than in Toronto (mean [SD]: 44 nm [25]). Extreme gradient boosting models performed best and explained the majority of spatiotemporal variations in outdoor UFP number concentrations (Montreal, R 2: 0.727; Toronto, R 2: 0.723) and UFP size (Montreal, R 2: 0.823; Toronto, R 2: 0.898) with slopes close to one and intercepts close to zero for relationships between measured and predicted values.

Conclusion

These new models will be applied in future epidemiological studies examining the acute health impacts of outdoor UFPs in Canada's two largest cities.

Surveillance-image-based outdoor air quality monitoring

Air pollution threatens human health, necessitating effective and convenient air quality monitoring. Recently, there has been a growing interest in using camera images for air quality estimation. However, a major challenge has been nighttime detection due to the limited visibility of nighttime images. Here we present a hybrid deep learning model, capitalizing on the temporal continuity of air quality changes for estimating outdoor air quality from surveillance images. Our model, which integrates a convolutional neural network (CNN) and long short-term memory (LSTM), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including PM2.5 and PM10 concentrations, as well as the air quality index (AQI). Compared to independent CNN networks that solely extract spatial features, our model demonstrates superior accuracy on self-constructed datasets with R2 = 0.94 and RMSE = 5.11 μg m-3 for PM2.5, R2 = 0.92 and RMSE = 7.30 μg m-3 for PM10, and R2 = 0.94 and RMSE = 5.38 for AQI. Furthermore, our model excels in daytime air quality estimation and enhances nighttime predictions, elevating overall accuracy. Validation across diverse image datasets and comparative analyses underscore the applicability and superiority of our model, reaffirming its applicability and superiority for air quality monitoring.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

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.

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

Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO₂ models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO₂. Our results suggest that street view imagery alone may provide sufficient information to explain NO₂ variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R² of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO₂ pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.

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.