[1] Four global scale and three regional scale chemical transport models are intercompared and evaluated during NASA's Transport and Chemical Evolution over the Pacific (TRACE-P) experiment. Model simulated and measured CO are statistically analyzed along aircraft flight tracks. Results for the combination of 11 flights show an overall negative bias in simulated CO. Biases are most pronounced during large CO events. Statistical agreements vary greatly among the individual flights. Those flights with the greatest range of CO values tend to be the worst simulated. However, for each given flight, the models generally provide similar relative results. The models exhibit difficulties simulating intense CO plumes. CO error is found to be greatest in the lower troposphere. Convective mass flux is shown to be very important, particularly near emissions source regions. Occasionally meteorological lift associated with excessive model-calculated mass fluxes leads to an overestimation of middle and upper tropospheric mixing ratios. Planetary Boundary Layer (PBL) depth is found to play an important role in simulating intense CO plumes. PBL depth is shown to cap plumes, confining heavy pollution to the very lowest levels.

An intercomparison and evaluation of aircraft-derived and simulated CO from seven chemical transport models during the TRACE-P experiment : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

Street view imagery in urban analytics and GIS: A review

Autonomous vehicles opportunities for cities air quality.

Land-use regression (LUR) models are frequently applied to estimate spatial patterns of air pollution. Traditional LUR often relies on fixed-site measurements and GIS-derived variables with limited spatial resolution. We present an approach that leverages Google Street View (GSV) imagery to predict street-level particulate air pollution (i.e., black carbon [BC] and particle number [PN] concentrations). We developed empirical models based on mobile monitoring data and features extracted from ∼52 500 GSV images using a deep learning model. We tested theory- and data-driven feature selection methods as well as models using images within varying buffer sizes (50-2000 m). Compared to LUR models with traditional variables, our models achieved similar model performance using the street-level predictors while also identifying additional potential hotspots. Adjusted R2 (10-fold CV R2) with integrated feature selection was 0.57-0.64 (0.50-0.57) and 0.65-0.73 (0.61-0.66) for BC and PN models, respectively. Models using only features near the measurement locations (i.e., GSV images within 250 m) explained ∼50% of air pollution variability, indicating PN and BC are strongly affected by the street-level built environment. Our results suggest that GSV imagery, processed with computer vision techniques, is a promising data source to develop LUR models with high spatial resolution and consistent predictor variables across administrative boundaries.

Using Street View Imagery to Predict Street-Level Particulate Air Pollution.

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

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 diff...

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

On‐road emission inventories in urban areas have typically been developed using traffic data derived from travel demand models. These approaches tend to underestimate emissions because the...

Methodology for spatio-temporal predictions of traffic counts across an urban road network and generation of an on-road greenhouse gas emission inventory

Arman Ganji

Papers

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

Methodology for spatio-temporal predictions of traffic counts across an urban road network and generation of an on-road greenhouse gas emission inventory

Determining the Effects of Automated Vehicle Driving Behavior on Vehicle Emissions and Performance of an Urban Corridor

Improved representation of surface-groundwater interaction in the Canadian land surface scheme