How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas.

This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5 years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals.

Mapping urban mobility using vehicle telematics to understand driving behaviour.

Telematics data, primarily collected from on-board vehicle devices (OBDs), has been utilised in this study to generate a thorough understanding of driving behaviour. The urban case study area is the large metropolitan region of the West Midlands, UK, but the approach is generalizable and translatable to other global urban regions. The new approach of GeoSpatial and Temporal Mapping of Urban Mobility (GeoSTMUM) is used to convert telematics data into driving metrics, including the relative time the vehicle fleet spends idling, cruising, accelerating, and decelerating.

Traffic Noise Assessment Using Intelligent Acoustic Sensors (Traffic Ear) and Vehicle Telematics Data.

Here, we introduce Traffic Ear, an acoustic sensor pack that determines the engine noise of each passing vehicle without interrupting traffic flow. The device consists of an array of microphones combined with a computer vision camera. The class and speed of passing vehicles were estimated using sound wave analysis, image processing, and machine learning algorithms.

Telematics data for geospatial and temporal mapping of urban mobility: Fuel consumption, and air pollutant and climate-forcing emissions of passenger cars.

In this study, we use the approach of geospatial and temporal (GeoST) mapping of urban mobility to evaluate the speed-time-acceleration profile (dynamic status) of passenger cars. We then use a pre-developed model, fleet composition and real-world emission factor (EF) datasets to translate vehicles dynamics status into real-urban fuel consumption (FC) and exhaustive (CO and NO) emissions with high spatial (15 m) and temporal (2 h) resolutions. Road transport in the West Midlands, UK, for 2016 and 2018 is the spatial and temporal scope of this study.

Detecting high emitting vehicle subsets using emission remote sensing systems.

It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed.

An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.

This study proposes a new model for the spatiotemporal prediction of PM concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU). The performance of the proposed model is boosted by learning spatial patterns from similar air quality (AQ) stations while maintaining long-term temporal dependencies with simultaneous learning and prediction for all stations over different time intervals.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK.

Real-world assessment of vehicle air pollutant emissions subset by vehicle type, fuel and EURO class: New findings from the recent UK EDAR field campaigns, and implications for emissions restricted zones.

This paper reports upon and analyses vehicle emissions measured by the Emissions Detecting and Reporting (EDAR) system, a Vehicle Emissions Remote Sensing System (VERSS) type device, used in five UK based field campaigns in 2016 and 2017. In total 94,940 measurements were made of 75,622 individual vehicles during the five campaigns. The measurements are subset into vehicle type (bus, car, HGV, minibus, motorcycle, other, plant, taxi, van, and unknown), fuel type for car (petrol and diesel), and EURO class, and particulate matter (PM), nitric oxide (NO) and nitrogen dioxide (NO) are reported.