Source Apportionment and Data Assimilation in Urban Air Quality Modelling for NO2: The Lyon Case Study

Impact of traffic on air pollution in a mid-sized urban city during COVID-19 lockdowns

In this study, we evaluated the changes in air pollutant concentrations around Milwaukee, WI, during and after lockdown due to the COVID-19 pandemic for a period of 126 days. Measurements of particulate matter (PM1, PM2.5, and PM10), NH3, H2S, and O3 + NO2, were made on a 74-km route of arterial and highway roads from April to August 2020 using a Sniffer 4D sensor mounted to a vehicle. Traffic volume during measurement periods were estimated from smartphone-based traffic data. From lockdown (March 24, 2020-June 11, 2020) to post-lockdown (June 12, 2020-August 26, 2020) median traffic volume increased roughly 30-84%, depending upon the road type. In addition, increases in mean concentrations of NH3 (277%), PM (220-307%), and O3 + NO2 (28%) were also observed. For both traffic and air pollutants, abrupt changes in the data were observed mid-June, shortly after lockdown measures were lifted in Milwaukee County. Indeed, traffic was able to explain up to 57% of PM, 47% of NH3, and 42% of O3 + NO2 variance in pollutant concentrations on arterial and highway road segments. Two arterial roads that did not have statistically significant changes in traffic patterns during the lockdown exhibited no statistically significant trends between traffic and air quality parameters. This study demonstrated that COVID-19 lockdowns in Milwaukee, WI, caused significant decreases in traffic, which in turn had a direct impact on air pollutants. It also highlights the need for traffic volume and air quality data at relevant spatial and temporal scales for accurately assessing source apportionment of combustion-based air pollutants, which cannot be captured with typical ground-based sensor systems.

Phenomenology of ultrafine particle concentrations and size distribution across urban Europe

The 2017-2019 hourly particle number size distributions (PNSD) from 26 sites in Europe and 1 in the US were evaluated focusing on 16 urban background (UB) and 6 traffic (TR) sites in the framework of Research Infrastructures services reinforcing air quality monitoring capacities in European URBAN & industrial areaS (RI-URBANS) project. The main objective was to describe the phenomenology of urban ultrafine particles (UFP) in Europe with a significant air quality focus. The varying lower size detection limits made it difficult to compare PN concentrations (PNC), particularly PN_10-25, from different cities. PNCs follow a TR > UB > Suburban (SUB) order. PNC and Black Carbon (BC) progressively increase from Northern Europe to Southern Europe and from Western to Eastern Europe. At the UB sites, typical traffic rush hour PNC peaks are evident, many also showing midday-morning PNC peaks anti-correlated with BC. These peaks result from increased PN_10-25, suggesting significant PNC contributions from nucleation, fumigation and shipping. Site types to be identified by daily and seasonal PNC and BC patterns are: (i) PNC mainly driven by traffic emissions, with marked correlations with BC on different time scales; (ii) marked midday/morning PNC peaks and a seasonal anti-correlation with PNC/BC; (iii) both traffic peaks and midday peaks without marked seasonal patterns. Groups (ii) and (iii) included cities with high insolation. PNC, especially PN_25-800, was positively correlated with BC, NO₂, CO and PM for several sites. The variable correlation of PNSD with different urban pollutants demonstrates that these do not reflect the variability of UFP in urban environments. Specific monitoring of PNSD is needed if nanoparticles and their associated health impacts are to be assessed. Implementation of the CEN-ACTRIS recommendations for PNSD measurements would provide comparable measurements, and measurements of <10 nm PNC are needed for full evaluation of the health effects of this size fraction.

Traffic-Related Air Pollution and Breast Cancer Risk: A Systematic Review and Meta-Analysis of Observational Studies

Current evidence of an association of breast cancer (BC) risk with air pollution exposure, in particular from traffic exhaust, remains inconclusive, and the exposure assessment methodologies are heterogeneous. This study aimed to conduct a systematic review and meta-analysis on the association between traffic-related air pollution (TRAP) and BC incidence (PROSPERO CRD42021286774). We systematically reviewed observational studies assessing exposure to TRAP and BC risk published until June 2022, available on Medline/PubMed and Web of Science databases. Studies using models for assessing exposure to traffic-related air pollutants or using exposure proxies (including traffic density, distance to road, etc.) were eligible for inclusion. A random-effects meta-analysis of studies investigating the association between NO₂/NO_x exposure and BC risk was conducted. Overall, 21 studies meeting the inclusion criteria were included (seven case-control, one nested case-control, 13 cohort studies); 13 studies (five case-control, eight cohort) provided data for inclusion in the meta-analyses. Individual studies provided little evidence of an association between TRAP and BC risk; exposure assessment methods and time periods of traffic emissions were different. The meta-estimate on NO₂ exposure indicated a positive association (pooled relative risk per 10 µg/m³ of NO₂: 1.015; 95% confidence interval, CI: 1.003; 1.028). No association between NO_x exposure and BC was found (three studies). Although there was limited evidence of an association for TRAP estimated with proxies, the meta-analysis showed a significant association between NO₂ exposure, a common TRAP pollutant marker, and BC risk, yet with a small effect size. Our findings provide additional support for air pollution carcinogenicity.

A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, like satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimation. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data is limited.

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.

On the mathematical modelling and data assimilation for air pollution assessment in the Tropical Andes

Air pollution assessment in the Tropical Andes requires a multidisciplinary approach. This can be supported from the understanding of the underlying biological dynamics and atmospheric behavior, to the mathematical approach for the proper use of all available information. This review paper touches on several aspects in which mathematical models can help to solve challenging problems regarding air pollution in reviewing the state-of-the-art at the global level and assessing the corresponding state of development as applied to the Tropical Andes. We address the complexities and challenges that modelling atmospheric dynamics in a mega-diverse region with abrupt topography entails. Understanding the relevance of monitoring and facing the problems of data scarcity, we call attention to the usefulness of data assimilation for uncertainty reduction, and how these techniques could help tackle the scarcity of regional monitoring networks to accelerate the implementation and development of modelling systems for air quality in the Tropical Andes. Finally, we suggest a cyberphysical framework for decision-making processes based on the data assimilation of chemical transport models, the forecast of scenarios, and their use in regulation and policy making.

A Combined Citizen Science—Modelling Approach for NO2 Assessment in Torino Urban Agglomeration

The #CHEARIATIRA citizen science campaign was developed in February 2019 in Torino (western part of the Po Valley megacity region). The aim of the campaign was public engagement with measuring NO2 concentrations in an urban area that often exceeds air quality standards. NO2 diffusion tubes were employed by citizens under our supervision. In this paper, we present the main outcomes of a combined approach between the #CHEARIATIRA campaign and the urban dispersion model SIRANE. The results were validated against the available public Air Quality Monitoring Stations (AQMS). The citizens’ passive samplers and the modelled data show a good response in central districts both during the campaign interval and by annual projection. Traffic hotspots and sensitive receptors (schools, hospital) have high concentrations of NO2. Most of the study area (83% of the tubes) is subject to an increased risk of premature death according to epidemiological literature.

Mesoscopic Urban-Traffic Simulation Based on Mobility Behavior to Calculate NOx Emissions Caused by Private Motorized Transport

Motorized transport is one of the main contributors to anthropogenic CO 2 emissions, which cause global warming. Other emissions, like nitrogen oxides or carbon monoxide, are detrimental to human health. A prominent way to understand and thus be able to minimize emissions is by using traffic simulations to evaluate different scenarios. In that way, one can find out which policies, technical innovations, or behavioral changes can lead to a decrease in emissions. Since the effect of CO 2 is on a global scale, a macroscopic model is often enough to find reasonable results. However, NO x emissions can also have a direct, local effect. Therefore, it is interesting to investigate these emissions on a mesoscopic scale, to gain insight into the local distribution of this pollutant. In this study, we used a traffic model that, contrary to most other state-of-the-art traffic simulations, does not require an origin–destination matrix as an input, but calculates it from mobility behavior extracted from a survey. We then generated agents with realistic mobility behavior that perform their daily trips and calculate key features like congestion and emissions for every edge of the road network. Our approach has the additional advantage of allowing to investigate technical, juridical, as well as behavioral changes, all within the same framework. It is then possible to identify strategies that minimize NO x emissions caused by private motorized transport. Evaluation showed good agreement with reality in terms of local and temporal resolution. Especially when looking at the sum of emissions, the main feature for evaluating policies, and deviations between our simulation and available statistics were negligible. We found that, from all scenarios we investigated, the ban of old diesel cars is the most promising policy: By replacing all diesel cars built in 2005 or earlier with petrol cars of the same age, NO x emissions could drop by roughly a third.

Recent Advances in Urban Ventilation Assessment and Flow Modelling

The Atmosphere Special Issue “Recent Advances in Urban Ventilation Assessment and FlowModelling” collects twenty-one original papers and one review paper published in 2017[...]

Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia

Ozone and fine particles (PM2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution was then modelled using the CCAM-CTM (Cubic Conformal Atmospheric model-Chemical Transport model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial and domestic sources, power generation was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information that policy makers can use to devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.