Impact of primary NO2 emissions at different urban sites exceeding the European NO2 standard limit

Constructing transferable and interpretable machine learning models for black carbon concentrations

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Distinct urban-rural gradients of air NO2 and SO2 concentrations in response to emission reductions during 2015-2022 in Beijing, China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are two major air pollutants in urban environment. Emission reduction policies have thus been implemented to improve urban air quality, especially in the metropolises. However, it remains unclear whether the air concentrations of NO2 and SO2 in and around large cities follow a same spatial pattern and how their characteristics change over time in response to the emission reductions. Using ground-based monitoring datasets of air NO2 and SO2 concentrations in Beijing, China, we tested the hypothesis of urban air pollutant islands and evaluated their seasonal and inter-annual variations during 2015-2022. The results showed that air NO2 concentrations increased significantly towards the urban core, being in line with the hypothesis of urban air pollutant island, while air SO2 concentrations showed no such spatial patterns. The urban air NO2 island varied seasonally, with larger radius and higher air NO2 concentrations in spring and winter. In response to the emission reduction, the annual mean radius of the urban air NO2 island showed a rapid decrease from 45.8 km to zero km during the study period. The annual mean air NO2 concentration at the urban core showed a linear decrease at a rate of 4.5 μg m-3 yr-1. In contrast, air SO2 concentration decreased nonlinearly over time and showed a legacy in comparison to the emission reduction. Our findings suggest different urban-rural gradients of air NO2 and SO2 concentrations and highlight their distinct responses to the regional reductions of anthropogenic emissions.

The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe

This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 and 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from fossil fuel combustion, mostly traffic (eBCT) was higher than that of residential and commercial sources (eBCRC) in all European sites studied. Nevertheless, eBCRC still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBCT over the last decade, while eBCRC remained relatively constant or even increased slightly in some cities.

Effect of different plant communities on NO2 in an urban road greenbelt in Nanjing, China

As an important part of urban ecosystems, plants can reduce NO₂ concentrations in the air. However, there is little evidence of the effects of different plant communities on NO₂ concentrations in street-scale green spaces. We used a multifunctional lifting environmental detector to investigate the impact of environmental factors and small plant communities on NO₂ concentrations in street green spaces during the summer and winter in Nanjing, China. The results showed that temperature, atmospheric pressure, and noise were significantly (P < 0.05) correlated with seasonal changes, temperature and humidity significantly (P < 0.01) influenced NO₂ concentrations in winter and summer, and the average NO₂ concentration in summer was generally higher than in winter. By comparing NO₂ concentrations in different plant community structures and their internal spaces, we found that the plant community structure with tree-shrub-grass was more effective in reducing pollution. These findings will help predict the impact of plant communities on NO₂ concentrations in urban streets and help city managers and planners effectively reduce NO₂ pollution.

Impact of urban aerosols on the cloud condensation activity using a clustering model

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (N_tot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high N_tot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large N_tot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

Comparison of static and dynamic exposures to air pollution, noise, and greenness among seniors living in compact-city environments

GPS technology and tracking study designs have gained popularity as a tool to go beyond the limitations of static exposure assessments based on the subject's residence. These dynamic exposure assessment methods offer high potential upside in terms of accuracy but also disadvantages in terms of cost, sample sizes, and types of data generated. Because of that, with our study we aim to understand in which cases researchers need to use GPS-based methods to guarantee the necessary accuracy in exposure assessment. With a sample of 113 seniors living in Barcelona (Spain) we compare their estimated daily exposures to air pollution (PM2.5, PM10, NO2), noise (dB), and greenness (NDVI) using static and dynamic exposure assessment techniques. Results indicate that significant differences between static and dynamic exposure assessments are only present in selected exposures, and would thus suggest that static assessments using the place of residence would provide accurate-enough values across a number of exposures in the case of seniors. Our models for Barcelona's seniors suggest that dynamic exposure would only be required in the case of exposure to smaller particulate matter (PM2.5) and exposure to noise levels. The study signals to the need to consider both the mobility patterns and the built environment context when deciding between static or dynamic measures of exposure assessment.

Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO₂ and PM₁₀ at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017-2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO₂ (R² = 0.72) than PM₁₀ concentrations (R² = 0.35). The average decrease of NO₂ was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM₁₀ concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.

Spatiotemporal variability of nitrogen dioxide (NO2) pollution in Manchester (UK) city centre (2017-2018) using a fine spatial scale single-NOx diffusion tube network

Nitrogen dioxide (NO₂) is linked to poor air quality and severe human health impacts, including respiratory and cardiovascular diseases and being responsible annually for approximately 23,500 premature deaths in the UK. Automated air quality monitoring stations continuously record pollutants in urban environments but are restricted in number (need for electricity, maintenance and trained operators), only record air quality proximal to their location and cannot document variability of airborne pollutants at finer spatial scales. As an alternative, passive sampling devices such as Palmes-type diffusion tubes can be used to assess the spatial variability of air quality in greater detail, due to their simplicity (e.g. small, light material, no electricity required) and suitability for long-term studies (e.g. deployable in large numbers, useful for screening studies). Accordingly, a one passive diffusion tube sampling approach has been adapted to investigate spatial and temporal variability of NO₂ concentrations across the City of Manchester (UK). Spatial and temporal detail was obtained by sampling 45 locations over a 12-month period (361 days, to include seasonal variability), resulting in 1080 individual NO₂ measurements. Elevated NO₂ concentrations, exceeding the EU/UK limit value of 40 µg m^-3, were recorded throughout the study period (N = 278; 26% of individual measurements), particularly during colder months and across a wide area including residential locations. Of 45 sampling locations, 24% (N = 11) showed annual average NO₂ above the EU/UK limit value, whereas 16% (N = 7) showed elevated NO₂ (> 40 µg m^-3) for at least 6 months of deployment. Highest NO₂ was recorded in proximity of highly trafficked major roads, with urban factors such as surrounding building heights also influencing NO₂ dispersion and distribution. This study demonstrates the importance of high spatial coverage to monitor atmospheric NO₂ concentrations across urban environments, to aid identification of areas of human health concern, especially in areas that are not covered by automated monitoring stations. This simple, reasonably cheap, quick and easy method, using a single-NO_x diffusion tube approach, can aid identification of NO₂ hotspots and provides fine spatial detail of deteriorated air quality. Such an approach can be easily transferred to comparable urban environments to provide an initial screening tool for air quality and air pollution, particularly where local automated air quality monitoring stations are limited. Additionally, such an approach can support air quality assessment studies, e.g. lichen or moss biomonitoring studies.

Effects analysis on hydrocarbon light-off performance of a catalytic gasoline particulate filter during cold start

In order to study the hydrocarbon combustion in the low-temperature catalytic process of a catalytic gasoline particulate filter (CGPF) during cold start, a mathematical model of the CGPF is established and verified firstly. Then, take T₅₀ (a temperature when the hydrocarbon conversion rate reaches 50%) as hydrocarbon light-off (LO) temperature; the effects of different exhaust parameters and structural parameters on hydrocarbon light-off performance and reaction rate are investigated based on simulation results. Finally, orthogonal experiment analysis is employed to further obtain the most significant factors and suggested parameter solution. The results show that the hydrocarbon LO performance of the CGPF during cold start is positively correlated with exhaust oxygen concentration, porosity, and filter length, but it is negatively correlated with exhaust flow rate and exhaust water vapor concentration. In addition, the inlet of the channel has a significant HC reaction when the oxygen concentration reaches 2.2%, and porosity mainly influences the front half part of the filter. Moreover, the influence degree relationship of the five factors is oxygen > mass flow > porosity > length > water vapor, and the optimum solution of length, vapor, mass flow, porosity, and oxygen is 150 mm, 12.31%, 0.002 kg/s, 0.55, and 2.2%, respectively. This work offers us great reference value for CGPF performance enhancement and hydrocarbon abatement of a GDI engine.

Ambient Size-Segregated Particulate Matter Characterization from a Port in Upstate New York

Air pollution impacts human health and the environment, especially in urban cities with substantial industrial activities and vehicular traffic emissions. Despite increasingly strict regulations put in place by regulatory agencies, air pollution is still a significant environmental problem in cities across the world. The objective of this study was to evaluate the environmental pollution from stationary and mobile sources using real-time monitoring and sampling techniques to characterize size-segregated particulate matter (PM), black carbon (BC), and ozone (O3) at the Port of Albany, NY. Air pollution monitoring was carried out for 3 consecutive weeks under a 24-hour cycle in 2018 at the New York State Department of Environmental Conservation (NYSDEC) site within the Port. Sampling was done with an AEROCET 531, optical particle sizer (OPS), ozone monitor, and MicroAeth AE51. Higher mass and number concentrations of size-segregated particles were observed during the daytime. PM2.5 and PM10 concentrations ranged from 1 to 271 micrograms per cubic meter (µg/m3) and 1 to 344 µg/m3, respectively. While these values do not exceed the level of the USEPA 24-hour standards, frequent sharp peaks were observed at higher concentrations. Size-segregated PM at sizes 0.3 µm and 0.374 µm recorded maximum concentrations of 101,631 particle number per cubic centimeter (#/cm3) and 43,432 #/cm3, respectively. Wide variations were observed in the particle number concentrations for 0.3 µm, 0.374 µm, and 0.465 µm sizes, which ranged from 1521 to 101,631 #/cm3; 656 to 43,432 #/cm3; and 311 to 29,271 #/cm3, respectively. BC concentration increased during morning and evening rush hours with the maximum concentration of 11,971 ng/m3 recorded at 8:00 AM. This suggests that mobile sources are the primary contributor to anthropogenic sources of BC within the Port. Episodic elevations in the concentrations of size-segregated PM and BC confirmed the contribution of industrial and vehicular activities around the Port of Albany. This study underscores the importance of measuring particles on a size-segregated basis in order to more fully understand the contributions of the multiple sources present within and surrounding a port environment.

Wind and Turbulence Statistics in the Urban Boundary Layer over a Mountain–Valley System in Granada, Spain

Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more complicated. This is the case of mid-latitude urban areas located in typical basin topographies, which usually present low winds and high turbulence within the atmospheric boundary layer (ABL). This study focuses on the analysis of the first ever measurements of wind with high temporal and vertical resolution throughout the ABL over a medium-sized city surrounded by mountains in southern Spain. These measurements have been gathered with a scanning Doppler lidar system and analyzed using the Halo lidar toolbox processing chain developed at the Finnish Meteorological Institute. We have used the horizontal wind product and the ABL turbulence classification product to carry out a statistical study using a two-year database. The data availability in terms of maximum analyzed altitudes for statistically significant results was limited to around 1000–1500 m above ground level (a.g.l.) due to the decreasing signal intensity with height that also depends on aerosol load. We have analyzed the differences and similarities in the diurnal evolution of the horizontal wind profiles for different seasons and their modelling with Weibull and von Mises probability distributions, finding a general trend of mean daytime wind from the NW with mean speeds around 3–4 m/s at low altitudes and 6–10 m/s at higher altitudes, and weaker mean nocturnal wind from the SE with similar height dependence. The highest speeds were observed during spring, and the lowest during winter. Finally, we studied the turbulent sources at the ABL with temporal (for each hour of the day) and height resolution. The results show a clear convective activity during daytime at altitudes increasing with time, and a significant wind-shear-driven turbulence during night-time.

Research on the Temporal and Spatial Characteristics of Air Pollutants in Sichuan Basin

Sichuan Basin is one of the most densely populated areas in China and the world. Human activities have great impact on the air quality. In order to understand the characteristics of overall air pollutants in Sichuan Basin in recent years, we analyzed the concentrations of six air pollutants monitored in 22 cities during the period from January 2015 to December 2020. During the study period, the annual average concentrations of CO, NO2, SO2, PM2.5 and PM10 all showed a clear downward trend, while the ozone concentration was slowly increasing. The spatial patterns of CO and SO2 were similar. High-concentration areas were mainly located in the western plateau of Sichuan Basin, while the concentrations of NO2 and particulate matter were more prominent in the urban agglomerations inside the basin. During the study period, changes of the monthly average concentrations for pollutants (except for O3) conformed to the U-shaped pattern, with the highest in winter and the lowest in summer. In the southern cities of the basin, secondary sources had a higher contribution to the generation of fine particulate matter, while in large cities inside the basin, such as Chengdu and Chongqing, air pollution had a strong correlation with automobile exhaust emissions. The heavy pollution incidents observed in the winter of 2017 were mainly caused by the surrounding plateau terrain with typical stagnant weather conditions. This finding was also supported by the backward trajectory analysis, which showed that the air masses arrived in Chengdu were mainly from the western plateau area of the basin. The results of this study will provide a basis for the government to take measures to improve the air quality in Sichuan Basin.

Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area

High-resolution air quality maps are critical towards assessing and understanding exposures to elevated air pollution in dense urban areas. However, these surfaces are rarely available in low- and middle-income countries that suffer from some of the highest air pollution levels worldwide. In this study, we make use of land use regressions (LURs) to generate annual and seasonal, high-resolution nitrogen dioxide (NO₂), nitrogen oxides (NO_x), and ozone (O₃) exposure surfaces for the Greater Beirut Area (GBA) in Lebanon. NO₂, NO_x and O₃ concentrations were monitored using passive samplers that were deployed at 55 pre-defined monitoring locations. The average annual concentrations of NO₂, NO_x, and O₃ across the GBA were 36.0, 89.7, and 26.9 ppb, respectively. Overall, the performance of the generated models was appropriate, with low biases, high model robustness, and acceptable R² values that ranged between 0.66 and 0.73 for NO₂, 0.56 and 0.60 for NO_x, and 0.54 and 0.65 for O₃. Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO₂ and NO_x levels in the GBA, whereas they acted as sinks for O₃ concentrations. No seasonally significant differences were found for the NO₂ and NO_x pollution surfaces; as their seasonal and annual models were largely similar (Pearson's r > 0.85 for both pollutants). On the other hand, seasonal O₃ pollution surfaces were significantly different. The model results showed that around 99% of the population of the GBA were exposed to NO₂ levels that exceeded the World Health Organization defined annual standard.

COVID-19 transport restrictions in Ireland: impact on air quality and respiratory hospital admissions

Aim

Exposure to poor air quality is a well-established factor for exacerbation of respiratory system diseases (RSDs); whether air pollutants are a cause of the development of RSD, however, remains unclear. This study aimed to examine the relationship between COVID-19 transport restrictions and hospital admissions because of RSD in Dublin city and county for 2020.

Study design

This was a retrospective population-based cohort.

Methods

Admission data were collected from the Health Service Executive Hospital In-patient Enquiry. Daily count of hospital admissions with Dublin city and county address with primary diagnosis of RSD was performed. The daily air nitrogen dioxide (NO₂) data were obtained from the Environmental Protection Agency (EPA).

Results

During the period of transport restrictions, there was a reduction in the annual mean NO₂ from 25 μg/m³ to 17 μg/m³ (P < 0.001), and decreases in hospital admissions for RSD were observed. Among the 9934 patient episodes included in this study, the mean age at admission was 61.5 years, 57.8% were female (n = 5744), and mean (standard deviation) length of stay was 7.5 (13.52) days.

Conclusion

This study, using routinely gathered data, suggests that decreases in ambient NO₂ as related to COVID-19 transport restrictions were significantly associated with lower asthma and chronic obstructive pulmonary disease admissions.

Quantifying traffic, biomass burning and secondary source contributions to atmospheric particle number concentrations at urban and suburban sites

In this study, we propose a new approach to determine the contributions of primary vehicle exhaust (N₁^ff), primary biomass burning (N₁^bb) and secondary (N₂) particles to mode segregated particle number concentrations. We used simultaneous measurements of aerosol size distribution in the 12-600 nm size range and black carbon (BC) concentration obtained during winter period at urban and suburban sites influenced by biomass burning (BB) emissions. As expected, larger aerosol number concentrations in the 12-25 and 25-100 nm size ranges are observed at the urban site compared to the suburban site. However, similar concentrations of BC are observed at both sites due to the larger contribution of BB particles to the observed BC at suburban (34%) in comparison to urban site (23%). Due to this influence of BB emissions in our study area, the application of the Rodríguez and Cuevas (2007) method, which was developed for areas mainly influenced by traffic emissions, leads to an overestimation of the primary vehicle exhaust particles concentrations by 18% and 26% in urban and suburban sites, respectively, as compared to our new proposed approach. The results show that (1) N₂ is the main contributor in all size ranges at both sites, (2) N₁^ff is the main contributor to primary particles (>70%) in all size ranges at both sites and (3) N₁^bb contributes significantly to the primary particles in the 25-100 and 100-600 nm size ranges at the suburban (24% and 28%, respectively) and urban (13% and 20%, respectively) sites. At urban site, the N₁^ff contribution shows a slight increase with the increase of total particle concentration, reaching a contribution of up to 65% at high ambient aerosol concentrations. New particle formation events are an important aerosol source during summer noon hours but, on average, these events do not implicate a considerable contribution to urban particles.

Activation properties of aerosol particles as cloud condensation nuclei at urban and high-altitude remote sites in southern Europe

Understanding the activation properties of aerosol particles as cloud condensation nuclei (CCN) is important for the climate and hydrological cycle, but their properties are not fully understood. In this study, the CCN activation properties of aerosols are investigated at two different sites in southern Spain: an urban background station in Granada and a high altitude mountain station in the Sierra Nevada National Park, with a horizontal separation of 21 km and vertical separation of 1820 m. CCN activity at the urban environment is driven by primary sources, mainly road traffic. Maximum CCN concentrations occurred during traffic rush hours, although this is also when the activation fraction is lowest. This is due to the characteristics of the rush hour aerosol consisting of ultrafine and less hygroscopic particles. In contrast, the mountain site exhibited larger and more hygroscopic particles, with CCN activity driven by the joint effect of new particle formation (NPF) and vertical transport of anthropogenic particles from Granada urban area by orographic buoyant upward flow. This led to the maximum concentrations of CCN and aerosol particles occurring at midday at the mountain site. Clear differences in the diurnal evolution of CCN between NPF events and non-event days were observed at the Sierra Nevada station, demonstrating the large contribution of NPF to CCN concentrations, especially at high supersaturations. The isolated contribution of NPF to CCN concentration has been estimated to be 175% higher at SS = 0.5% relative to what it would be without NPF. We conclude that NPF could be the major source of CCN at this mountain site. Finally, two empirical models were used to parameterize CCN concentration in terms of aerosol optical or physical parameters. The models can explain measurements satisfactorily at the urban station. At the mountain site both models cannot reproduce satisfactorily the observations at low SS.

A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City

Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control.

SnO2/TiO2 Thin Film n-n Heterostructures of Improved Sensitivity to NO2

Thin-film n-n nanoheterostructures of SnO₂/TiO₂, highly sensitive to NO₂, were obtained in a two-step process: (i) magnetron sputtering, MS followed by (ii) Langmuir-Blodgett, L–B, technique. Thick (200 nm) SnO₂ base layers were deposited by MS and subsequently overcoated with a thin and discontinuous TiO₂ film by means of L–B. Rutile nanopowder spread over the ethanol/chloroform/water formed a suspension, which was used as a source in L–B method. The morphology, crystallographic and electronic properties of the prepared sensors were studied by scanning electron microscopy, SEM, X-ray diffraction, XRD in glancing incidence geometry, GID, X-ray photoemission spectroscopy, XPS, and uv-vis-nir spectrophotometry, respectively. It was found that amorphous SnO₂ films responded to relatively low concentrations of NO₂ of about 200 ppb. A change of more than two orders of magnitude in the electrical resistivity upon exposure to NO₂ was further enhanced in SnO₂/TiO₂ n-n nanoheterostructures. The best sensor responses R_NO2/R₀ were obtained at the lowest operating temperatures of about 120 °C, which is typical for nanomaterials. Response (recovery) times to 400 ppb NO₂ were determined as a function of the operating temperature and indicated a significant decrease from 62 (42) s at 123 °C to 12 (19) s at 385 °C A much smaller sensitivity to H₂ was observed, which might be advantageous for selective detection of nitrogen oxides. The influence of humidity on the NO₂ response was demonstrated to be significantly below 150 °C and systematically decreased upon increase in the operating temperature up to 400 °C.

Enhanced High-Temperature (600 °C) NO2 Response of ZnFe2O4 Nanoparticle-Based Exhaust Gas Sensors

Fabrication of gas sensors to monitor toxic exhaust gases at high working temperatures is a challenging task due to the low sensitivity and narrow long-term stability of the devices under harsh conditions. Herein, the fabrication of a chemiresistor-type gas sensor is reported for the detection of NO₂ gas at 600 °C. The sensing element consists of ZnFe₂O₄ nanoparticles prepared via a high-energy ball milling and annealed at different temperatures (600–1000 °C). The effects of annealing temperature on the crystal structure, morphology, and gas sensing properties of ZnFe₂O₄ nanoparticles are studied. A mixed spinel structure of ZnFe₂O₄ nanoparticles with a lattice parameter of 8.445 Å is revealed by X-ray diffraction analysis. The crystallite size and X-ray density of ZnFe₂O₄ nanoparticles increase with the annealing temperature, whereas the lattice parameter and volume are considerably reduced indicating lattice distortion and defects such as oxygen vacancies. ZnFe₂O₄ nanoparticles annealed at 1000 °C exhibit the highest sensitivity (0.13% ppm^–1), sharp response (τ_res = 195 s), recovery (τ_rec = 17 s), and linear response to 100–400 ppm NO₂ gas. The annealing temperature and oxygen vacancies play a major role in determining the sensitivity of devices. The plausible sensing mechanism is discussed. ZnFe₂O₄ nanoparticles show great potential for high-temperature exhaust gas sensing applications.

São Paulo's atmospheric pollution reduction and its social isolation effect, Brazil

Since January 2020, studies report reductions in air pollution among several countries due to social isolation measures, which have been adopted in order to contain the coronavirus outbreak progress (COVID-19). This study aims to evaluate the change in the atmospheric pollution levels by NO and NO2 in São Paulo City for the social isolation period. The NO and NO2 hourly concentrations were obtained through air quality monitoring stations from CETESB, from January 14, 2020 to April 12, 2020. Mann-Kendall and the Pettitt tests were performed in the air pollutant time series. We observed an overall negative trend in all stations, indicating a decreasing temporal pattern in concentrations. Regarding NO, the highest absolute decrease rates were observed in the Congonhas (- 6.39 μg m-3 month-1) and Marginal Tietê (- 6.19 μg m-3 month-1) stations; regarding NO2, the highest rates were observed in the Marginal Tietê (- 4.45 μg m-3 month-1) and Cerqueira César (- 4.34 μg m-3 month-1) stations. In addition, we identified a turning point in the NO and NO2 series trends that occurred close to the start date of the social isolation period (March 20, 2020). Moreover, from statistical analysis, it was found that NO2 is a suitable surrogate for monitoring economic activities during social isolation periods. Thus, we concluded that social isolation measures implemented on March 20, 2020 caused significant changes in the air pollutant concentrations in the city of São Paulo (as high as - 200% in NO2 levels).

Role of Meteorological Parameters in the Diurnal and Seasonal Variation of NO2 in a Romanian Urban Environment

The main purpose of this study was to investigate whether meteorological parameters (temperature, relative humidity, direct radiation) play an important role in modifying the NO₂ concentration in an urban environment. The diurnal and seasonal variation recorded at a NO₂ traffic station was analyzed, based on data collected in situ in a Romanian city, Braila (45.26° N, 27.95° E), during 2009-2014. The NO₂ atmospheric content close to the ground had, in general, a summer minimum and a late autumn/winter maximum for most years. Two diurnal peaks were observed, regardless of the season, which were more evident during cold months. Traffic is an important contributor to the NO₂ atmospheric pollution during daytime hours. The variability of in situ measurements of NO₂ concentration compared relatively well with space-based observations of the NO₂ vertical column by the Ozone Monitoring Instrument (OMI) satellite for most of the period under scrutiny. Data for daytime and nighttime (when the traffic is reduced) were analyzed separately, in the attempt to isolate meteorological effects. Meteorological parameters are not fully independent and we used partial correlation analysis to check whether the relationships with one parameter may be induced by another. The correlation between NO₂ and temperature was not coherent. Relative humidity and solar radiation seemed to play a role in shaping the NO₂ concentration, regardless of the time of day, and these relationships were only partially interconnected.

Long-Term Assessment of Air Quality and Identification of Aerosol Sources at Setúbal, Portugal

Understanding air pollution in urban areas is crucial to identify mitigation actions that may improve air quality and, consequently, minimize human exposure to air pollutants and their impact. This study aimed to assess the temporal evolution of the air quality in the city of Setúbal (Portugal) during a time period of 10 years (2003–2012), by evaluating seasonal trends of air pollutants (PM₁₀, PM_2.5, O₃, NO, NO₂ and NO_x) measured in nine monitoring stations. In order to identify emission sources of particulate matter, PM_2.5 and PM_2.5–10 were characterized in two different areas (urban traffic and industrial) in winter and summer and, afterwards, source apportionment was performed by means of Positive Matrix Factorization. Overall, the air quality has been improving over the years with a decreasing trend of air pollutant concentration, with the exception of O₃. Despite this improvement, levels of PM₁₀, O₃ and nitrogen oxides still do not fully comply with the requirements of European legislation, as well as with the guideline values of the World Health Organization (WHO). The main anthropogenic sources contributing to local PM levels were traffic, industry and wood burning, which should be addressed by specific mitigation measures in order to minimize their impact on the local air quality.

Operational functionalities of air-quality WSn metal-oxide sensors correlating semiconductor defect levels and surface potential barriers

The comprehension of atmospheric pollution levels worldwide is a crucial issue for assessing the health consequences from human exposure to polluted air, and for identifying emission reductions that will be effective for minimizing the potential risks. Advanced interconnected sensors are required that could monitor on real-time toxic gaseous concentration. The success of these instruments depends on the reliability of these devices to quantify and disseminate the pollution levels to nearby citizens. Metal-oxide semiconductors are widely used as functional materials for gas sensing because of their chemo-resistive effect when interacting with gases. Mixed oxides are usually considered for the superior performances shown with respect to the single oxides. In this work, tungsten-tin mixed oxides with different Sn molar fraction (0.0018, 0.12, 0.33 and 0.89 named WS-1, WS-2, WS-3 and WS-4) were synthesized by sol-gel co-precipitation. The powders were characterized by ICP-OES analysis, specific surface area measurements, electron microscopy, X-ray diffraction, UV-Vis-NIR and FT-IR spectroscopies. The powders were also deposited through screen-printing technology, obtaining thick film gas sensors, on which measurements of conductance as a function of temperature, surface potential barrier and dynamical responses in the presence of oxidizing or reducing gases were carried out. Based on the studied properties, the mixed oxides can be divided into two groups: the WO₃-like samples (WS-1, WS-2, WS-3) and the SnO₂-like sample (WS-4). All samples present pure crystalline structures: this is a new result for the WO₃-like samples. There is no literature data reporting about the introduction of so high Sn content in a WO₃ structure. The combination of spectroscopic and electrical characterizations allowed the definition of an interpretative model that correlates the deepness of defect levels in the band gap of these materials to the values of the surface potential barrier in air and, as a consequence, to the electrical responses to oxidizing and reducing gases.

Long-term trends in nitrogen oxides at different types of monitoring stations in the Czech Republic

Nitrogen oxides (NO_x) are important in atmospheric chemistry and have substantial environmental impacts. This study provides a detailed data analysis on long-term changes in ambient NO_x levels within the framework of their emission pattern. We examined the trends in NO_x, NO, NO₂ and NO₂/NO_x ratio at 39 Czech sites representing different environments (urban, rural, mountain, industrial) in 1994-2016, i.e. a 23-year time series, using the non-parametric Man-Kendall test. The ambient air concentrations in NO, NO₂ and NO_x decreased significantly at most of the sites, as was assumed due to the substantial NO_x emission decrease over the period under review. The largest decrease per year was detected at the urban site in capital Prague (decrease in the 98^th percentile equal to 2.28 ppb, decrease in the annual median equal to 1.00 ppb). At some sites, however we observed a substantial equivocal temporal change in the NO₂/NO_x ratio. Whereas at some sites the NO₂/NO_x ratio was decreasing, at other sites the trend was increasing significantly. The highest increase per year in the annual median of the NO₂/NO_x ratio, equal to 0.0086 was detected at the urban Praha 1-nám-Rep. site; the highest increase per year in the 98^th percentile, equal to 0.0126 was recorded at the rural Krupka site. Most sites (regardless of type) with generally increasing trends in the NO₂/NO_x ratio, are situated in the north-west portion of the Czech Republic The increasing NO₂/NO_x ratio detected at some sites is believed to implicate undesired changes in atmospheric chemistry, namely with respect to ambient O₃ formation, promoting increasing O₃ concentrations.

Urban vegetation and particle air pollution: Experimental campaigns in a traffic hotspot

This work presents the main results of two experimental campaigns carried out in summer and winter seasons in a complex pollution hotspot near a large park, El Retiro, in Madrid (Spain). These campaigns were aimed at understanding the microscale spatio-temporal variation of ambient concentration levels in areas with high pollution values to obtain data to validate models on the effect of urban trees on particulate matter concentrations. Two different measuring approaches have been used. The first one was static, with instruments continuously characterizing the meteorological variables and the particulate matter concentration outside and inside the park. During the summer campaign, the particulate matter concentration was clearly influenced by a Saharan dust outbreak during the period 23 June to 10 July 2016, when most of the particulate matter was in the fraction PM_2.5-10. During the winter campaign, the mass concentrations were related to the meteorological conditions and the high atmospheric stability. The second approach was a dynamic case with mobile measurements by portable instruments. During the summer campaign, a DustTrak instrument was used to measure PM₁₀ and PM_2.5 in different transects close to and inside the park at different distances from the traffic lane. It was observed a decrease in the concentrations up to 25% at 20 m and 50% at 200 m. High PM₁₀ values were linked to dust resuspension caused by recreational activities and to a Saharan dust outbreak. The highest PM values were measured at the Independencia square, an area with many bus stops and high traffic density. During the winter campaign, three microaethalometers were used for Black Carbon measurement. Both pollutants also showed a reduction in their concentrations when moving towards inside the park. For PM₁₀ and PM_2.5, reductions up to 50% were observed, while for BC this reduction was smaller, about 20%.