Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Assessing the impact of different urban morphology scenarios on air pollutant emissions distribution

As urban areas grow with the increase in population, so do the problems associated with these areas, such as an increase in atmospheric emissions. Since urban morphology has an effect on the environment, it is necessary to design future urban morphologies to accommodate the expected growth and mitigate the associated problems. By employing an emission distribution methodology based on the relationship between land use and emission activity sectors, including transport and road traffic emissions modelling (with PTV-VISUM and TREM, respectively), this study aims to identify urban morphologies that have the potential to minimize atmospheric emissions for future multi-core regions. This study assesses three urban morphology scenarios, focused on Aveiro, Portugal, where two represent urban compaction - Focused City scenario and Independent City scenario -, and one represents an extreme version of the current urban dispersion. The impact of urban scenarios was compared against the current urban morphology. Results indicate that, for the compact urban morphologies, the Focused City scenario showed a small increase in emissions, and the Independent City scenario led to a decrease in emissions, especially for NOx (-16 %), as it is the pollutant most affected by road traffic emissions. As for the Disperse City scenario, it showed the highest overall increase, as it greatly increased the vehicle volume and total distance travelled. These results highlight the need for policy and behavioral changes to accompany the changes to urban morphology, and for special attention to be paid to the location of activity sectors when designing the different urban morphologies. This study contributes novel insights by applying a comprehensive methodology that integrates land use, activity sectors, and road traffic emissions modelling. By assessing the urban morphology's impact on air pollutant emissions, it is possible to inform urban planners of future urban planning strategies.

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.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

Changes in Air Quality Associated with Mobility Trends and Meteorological Conditions during COVID-19 Lockdown in Northern England, UK

The COVID-19 pandemic triggered catastrophic impacts on human life, but at the same time demonstrated positive impacts on air quality. In this study, the impact of COVID-19 lockdown interventions on five major air pollutants during the pre-lockdown, lockdown, and post-lockdown periods is analysed in three urban areas in Northern England: Leeds, Sheffield, and Manchester. A Generalised Additive Model (GAM) was implemented to eliminate the effects of meteorological factors from air quality to understand the variations in air pollutant levels exclusively caused by reductions in emissions. Comparison of lockdown with pre-lockdown period exhibited noticeable reductions in concentrations of NO (56.68–74.16%), NO2 (18.06–47.15%), and NOx (35.81–56.52%) for measured data. However, PM10 and PM2.5 levels demonstrated positive gain during lockdown ranging from 21.96–62.00% and 36.24–80.31%, respectively. Comparison of lockdown period with the equivalent period in 2019 also showed reductions in air pollutant concentrations, ranging 43.31–69.75% for NO, 41.52–62.99% for NOx, 37.13–55.54% for NO2, 2.36–19.02% for PM10, and 29.93–40.26% for PM2.5. Back trajectory analysis was performed to show the air mass origin during the pre-lockdown and lockdown periods. Further, the analysis showed a positive association of mobility data with gaseous pollutants and a negative correlation with particulate matter.