Air Pollution In Cities Research Articles

Forecasting air pollutants using classification models: a case study in the Bay of Algeciras (Spain)

The main goal of this work is to obtain reliable predictions of pollutant concentrations related to maritime traffic (SO2, PM10, NO2, NOX, and NO) in the Bay of Algeciras, located in Andalusia, the south of Spain. Furthermore, the objective is to predict future air quality levels of the principal maritime traffic-related pollutants in the Bay of Algeciras as a function of the rest of the pollutants, the meteorological variables, and vessel data. In this sense, three scenarios were analysed for comparison, namely Alcornocales Park and the cities of La Línea and Algeciras. A database of hourly records of air pollution immissions, meteorological measurements in the Bay of Algeciras region and a database of maritime traffic in the port of Algeciras during the years 2017 to 2019 were used. A resampling procedure using a five-fold cross-validation procedure to assure the generalisation capabilities of the tested models was designed to compute the pollutant predictions with different classification models and also with artificial neural networks using different numbers of hidden layers and units. This procedure enabled appropriate and reliable multiple comparisons among the tested models and facilitated the selection of a set of top-performing prediction models. The models have been compared using several quality classification indexes such as sensitivity, specificity, accuracy, and precision. The distance (d1) to the perfect classifier (1, 1, 1, 1) was also used as a discriminant feature, which allowed for the selection of the best models. Concerning the number of variables, an analysis was conducted to identify the most relevant ones for each pollutant. This approach aimed to obtain models with fewer inputs, facilitating the design of an optimised monitoring network. These more compact models have proven to be the optimal choice in many cases. The obtained sensitivities in the best models were 0.98 for SO2, 0.97 for PM10, 0.82 for NO2 and NOX, and 0.83 for NO. These results demonstrate the potential of the models to forecast air pollution in a port city or a complex scenario and to be used by citizens and authorities to prevent exposure to pollutants and to make decisions concerning air quality.

Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021

Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021

Konya'da kentsel ısı adası ve karbon monoksit değişiminin Google Earth Engine kullanılarak incelenmesi

The increasing population has been causing changes in and around urban areas. As a result of this situation, it is observed that the amount of heat and air pollution in cities is higher than in rural areas. Urban heat islands (UHI) are a factor that affects people's quality of life. Therefore, monitoring temperature changes and taking regional measures is necessary. 
 In this study, the Landsat 8 satellite was used to determine the UHI, and the Sentinel-5P satellite was used to determine carbon monoxide (CO) pollution between 2019-2021. The central districts of Konya have been selected as the study area. To determine UHI between the seasons, a total of 12 land surface temperature (LST) maps were generated. In addition, 36 CO maps of twelve months were created for the same region using the Sentinel-5P satellite. When the maps created were evaluated, it was concluded that there was a significant result between temperature and CO. It was found that the areas with higher ST also had higher levels of CO.

Aerosol pollution of the Moscow megacity by polyaromatic hydrocarbons: Seasonal variability and toxicological risks

Research on air pollution in large cities by polycyclic aromatic hydrocarbons (PAHs) is one of the priority tasks for assessing air quality and environmental risks to public health. The chemical composition of aerosols sampled in spring (2018), 2019, and winter (2019–2020) at the Aerosol Complex of Moscow State University, located on the background territory of the Moscow Megacity, is analyzed. Sixteen priority PAH compounds were identified using gas chromatography, mass spectrometry, and high-performance liquid chromatography. The median value of the total concentration of the 16 PAHs (Σ16PAH) increases from the spring season (1.43 ng/m3) to the fall season (1.68 ng/m3) and then to the winter season (2.47 ng/m3). Based on the diagnostic relationships of PAHs, the dominant contribution of transport, industrial enterprises, and the heating system to the total emissions was determined. Pollution roses indicate the location of sources of maximum concentrations of low-, medium-, and high-molecular PAHs. Pollution episodes are distinguished: in the spring of 2018 under the influence of the transport of smoke plumes of agricultural fires and in the fall of 2019 as a result of petrogenic emissions and an increase in biomass burning in the residential sector around Moscow. In the winter and fall seasons, the highest values of carcinogenic (0.45 and 0.42) and mutagenic (0.58 and 0.55) equivalents for benzo(a)pyrene were recorded in comparison with the spring season (0.26 and 0.38). The lifetime risk of developing lung cancer, calculated from the data for three seasons, is 0.5 cases per one million people.

Interpretation of odor complaint records with BTEX pollutants and meteorological factors: Çorlu case study

Air pollution in urban areas increases as a result of emitted air from different sources within the mixing layer of troposphere. Odor pollution is amongst the primary reasons behind environmental nuisance and occurrence of citizen complaints. Frequent exposure to odorous compounds and/or odor nuisance are increasingly associated with air pollution problems. Besides, there is no universally accepted environmental odor management method reported so far. Level of air pollutants emission, distance of emission sources to residential areas, to-pography, geographical and meteorological conditions have influence on imposed level of air pollution and odor pollution in cities.
 
 This study is built on the citizen odor complaint data (based on frequency, intensity, duration, odor tone and location (namely the FIDOL factors) collected in Çorlu/Tekirdağ through the GIS integrated public participated platform, namely the Çorlu KODER mobile application. The annual odor complaint data was briefly introduced and given an evaluation with the mo-bile app users demographic information. The obtained data between August 28-November 2 of 2021, was subjected to interpretative evaluation and statistical analysis with BTEX (benzene, toluene, ethylene benzene and xylen) concentrations, inorganic air pollution concentrations and meteorological factors. In light of the obtained results, temperature, wind speed, relative humidity and toluene concentration were found to play a significant role on the number of citizen odor complaints. The EU reported limit value, lower rating threshold and upper rating threshold for BTEX pollutants have been exceeded several times.
 
 The average Toluene/benzene ratios obtained during the study show that; non-traffic sources contribute significantly to VOC emissions. Air pollutants transportation mechanism from neighbouring OIZ settlements become a prominent justification and support the hypothesis that residential areas of Corlu are under the effect of industrial air pollution and odor pollution con-stituents. There is low level of negative correlation between the benzene measured in Çorlu and WS (r=-0.63). Below 2.4 m/s, the average number of odor complaints (ANOC) tend to increase, while above this level odor complaints are diminished). The ANOC remained around 4 for[C] < 4 ug/m3 and reached to 18 for [C]BTEXtotal > 8 ug/m3. Above [C]Toluen=3 ug/m3 conditions, Daily ANOC increase from 7 to 19. Over [C]Toluen= 4 ug/m3 conditions, it reached up to 23. Increses in the number of daily ANOC by temperature is distinct over 21 C0 and reaches to 35. 
 
 The GIS integrated citizen complaint collection platforms are critical for real-time data collection of environmental complaints with high spatiotemporal accuracy. Citizen odor complaint surveys are useful monitoring tools and obtained data sets can be used to identify sensitive areas where and when specific actions should be taken and air pollutants measuement studies be performed.

Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment

Ambient air pollution is a major risk to health and wellbeing in European cities. We aimed to estimate spatial and sector-specific contributions of emissions to ambient air pollution and evaluate the effects of source-specific reductions in pollutants on mortality in European cities to support targeted source-specific actions to address air pollution and promote population health. We conducted a health impact assessment of data from 2015 for 857 European cities to estimate source contributions to annual PM2·5 and NO2 concentrations using the Screening for High Emission Reduction Potentials for Air quality tool. We evaluated contributions from transport, industry, energy, residential, agriculture, shipping, and aviation, other, natural, and external sources. For each city and sector, three spatial levels were considered: contributions from the same city, the rest of the country, and transboundary. Mortality effects were estimated for adult populations (ie, ≥20 years) following standard comparative risk assessment methods to calculate the annual mortality preventable on spatial and sector-specific reductions in PM2·5 and NO2. We observed strong variability in spatial and sectoral contributions among European cities. For PM2·5, the main contributors to mortality were the residential (mean contribution of 22·7% [SD 10·2]) and agricultural (18·0% [7·7]) sectors, followed by industry (13·8% [6·0]), transport (13·5% [5·8]), energy (10·0% [6·4]), and shipping (5·5% [5·7]). For NO2, the main contributor to mortality was transport (48·5% [SD 15·2]), with additional contributions from industry (15·0% [10·8]), energy (14·7% [12·9]), residential (10·3% [5·0]), and shipping (9·7% [12·7]). The mean city contribution to its own air pollution mortality was 13·5% (SD 9·9) for PM2·5 and 34·4% (19·6) for NO2, and contribution increased among cities of largest area (22·3% [12·2] for PM2·5 and 52·2% [19·4] for NO2) and among European capitals (29·9% [12·5] for PM2·5 and 62·7% [14·7] for NO2). We estimated source-specific air pollution health effects at the city level. Our results show strong variability, emphasising the need for local policies and coordinated actions that consider city-level specificities in source contributions. Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making 2023-2026 Horizon Europe project.

Street-scale dispersion modelling framework of road-traffic derived air pollution in Hanoi, Vietnam

Traffic is an important source of air pollution in Vietnamese cities. The spatio-temporal variation of air pollution derived from traffic is poorly understood. Application of dispersion modelling can help but is hindered by the local scarcity of suitable input data. This study fills the data gap, by establishing a framework employing open-access global data to model emission from traffic activities in Hanoi. The outlined methodology explicitly defines road sources, calculates their emission, and employs background pollution profiles from Copernicus Atmospheric Monitoring Service (CAMS) to produce street-scale distribution maps for CO, PM10 and PM2.5. Pollution hotspots are found near major traffic flows with the highest hourly average CO, PM10 and PM2.5 concentrations at 1206, 87.5 and 61.5 μgm−3, respectively. The relationship between concentrations and properties of the road network is assessed. Motorcycles are the main emitters of the traffic sector. Emission from Heavy Good Vehicles dominate during the night, with contribution percentages increase as it gets further away from the city core. Modelled concentrations are underestimated mainly due to low vehicular emission factor. Adjusting emission factors according to vehicle quality in Vietnam greatly improves agreement. The presence of non-traffic emission sources contributes to the model underestimation. Results for comparisons of daily averaged PM values are broadly in agreement between models and observations; however, diurnal patters are skewed. This results partly from the uncertainties linked with background pollution levels from CAMS, and partly from non-traffic sources which are not accounted for here. Further work is needed to assess the use of CAMS's concentrations in Vietnam. Meteorological input contributes to the temporal disagreement between the model and observations. The impact is most noticeable with CO concentrations during morning traffic rush hours. This study recommends approaches to improve input for future model iterations and encourage applications of dispersion modelling studies in similar economic settings.

Whether the Establishment of National High-Tech Zones Can Improve Urban Air Pollution: Empirical Evidence from Prefecture-Level Cities in China

Promoting the construction of national-level high-tech zones as green pioneer zones is a necessary condition for achieving high-quality development. Based on panel data from 254 prefecture-level cities from 2006 to 2018, in this paper, the difference-in-difference (DID) is used to empirically examine the influence effect of the establishment of national high-tech zones on local urban air pollution, and the spatial difference-in-difference (SDID) is used to explore its spatial spillover effect. It was found that the annual average PM2.5 concentration in cities with national high-tech zones decreased by about 1.8% compared with cities without national high-tech zones, and there was a positive spillover effect on the annual average PM2.5 concentration in nearby cities. Industrial structure upgrading and technological innovation effects are two important transmission paths for the establishment of national high-tech zones to influence urban air pollution; the heterogeneity analysis shows that the establishment of national high-tech zones has more significant implications for the improvement of air pollution in non-resource cities and less developed areas in the west, and the air pollution improvement effect of “growing” national high-tech zones is more desirable than that of “mature” national high-tech zones. Our empirical results conclude that we should continue to encourage the promotion of national high-tech zones, optimize the business environment, improve preferential policies, and design a combination of policy instruments scientifically according to local conditions in order to give full play to the effect of national high-tech zones on urban air-pollution improvement.

Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India

Rapid development and urbanization in most of the cities of the world have led to increased vehicular traffic, industrial activities, and construction. Air pollution in most cities of developing countries, like India, often exceeds the limits given in relevant national/international standards. Rapid urbanization in Bengaluru is causing increased air pollution and land surface temperature (LST). The present study examines the relationship of LST with air pollutants, meteorological parameters, surface indices, and selected urbanization parameters. The study also aims to find out the air pollutant have the maximum effect on LST during different seasons. Air pollutants and meteorological data were collected from air quality monitoring stations, while LST data of MODIS satellite was downloaded from the internet. Data for the period 2018–2022 was utilized for the current research. The results indicated that PM2.5 concentrations exceeded WHO standards, and Hombegowda Nagar (B9), Peenya (B5), and Bapuji Nagar (B4) were identified as pollution hot-spots. A strong association of LST with air pollutants and meteorological parameters was observed during the winter season (R2 = 0.87 to 0.91), which was stronger than the corresponding association during summer (R2 = 0.72 to 0.79). The association further improved after incorporating spectral indices (NDVI and NDBI), global population density (GPD), digital elevation model (DEM), and road line density (RLD) and a very strong association was obtained (R2 value of 0.91 in summer and 0.96 in winter). The study also revealed a strong relationship between the Urban Heat Island index and the combination of all parameters, both for urban and rural areas as well as during both the summer and winter seasons. PM2.5 and SO2 were identified as the main air pollutants affecting LST variations during the summer season, while NO2 and CO were more influential during the winter season. The study concludes that variation in the LST of an area is governed not only by the surface characteristics but it is also affected by air pollutants and meteorological conditions as well as conditions of urbanization that can be represented by different parameters/indices. The findings of this study hold significant implications for future scientific research, and it may pave the way for the integration of effective mitigation strategies to combat the dual challenges of increasing LST and air pollution in urban areas.

Regional Representativeness Analysis of Ground-Monitoring PM2.5 Concentration Based on Satellite Remote Sensing Imagery and Machine Learning Techniques

The issue of urban air quality in China has become increasingly significant due to industrialization and rapid urbanization. Although PM2.5 is the major air pollutant in most cities of northern China and has a direct negative impact on human health, there is a problem of under-representativeness at Chinese monitoring stations. In some cities, due to the relatively fewer national control stations and the fact that the stations are located closer to pollution sources, under the current assessment system, the monitoring data are not sufficient for the fairness of air quality assessment in different cities. In this article, the multispectral data of Landsat 8 data, air quality data, and meteorological data from ground monitoring stations have been integrated together and imported to different PM2.5-estimation models established based on the multi-layer back propagation neural network (MLBPN), support vector regression (SVR), and random forest (RF), respectively. According to the evaluation indices of R2, RMSE, and ME, the estimation model based on the MLBPN revealed the best PM2.5 estimation results and was therefore employed for the regional representativeness analysis in the study area of Xi’an, Shaanxi, China. The annual average PM2.5 concentration in the study area is depicted after error correction using Kriging interpolation, which can be further used to evaluate and analyze the representativeness of monitoring stations in Xi’an. By calculating the difference between the actual station annual average and the annual average of estimated PM2.5 concentration in the whole region, it can be found that the regional annual average value of PM2.5 in Xi’an is overestimated. To sum up, this article proposes a feasible method for the spatial positioning of the air quality monitoring stations to be established.

Health risk assessment from exposure to ambient VOCs and particulate matter in different functional zones in Dhanbad, India

This study evaluates the non-cancer risks (NCR) and cancer risks (CR) in outdoor working personnel in Dhanbad city, exposed to volatile organic compounds (VOCs) and particulate matter (PM) present in the ambient air. Dhanbad is known for its coal mines and is one of the most polluted cities in India and the world. Sampling was conducted in different functional zones; namely, traffic intersections, industrial, and institutional areas, to estimate the concentration of different PM-bound heavy metals and VOCs in the ambient air using Inductively coupled plasma-optical emission spectrometry (ICP-OES) and gas chromatography (GC) respectively. Our results show that the concentration levels (of VOCs and PM) and health risks were maximum at the traffic intersection area, followed by the industrial and institutional areas. The major contribution to CR came from chloroform, naphthalene, and PM-bound chromium, while the contribution to NCR mainly came from naphthalene, trichloroethylene, xylenes, and PM-bound chromium, nickel, and cadmium. It was observed that CR and NCR from VOCs are quite comparable (average CRvoc 8.92E-05 and NCRvoc 6.82) to that from the PM-bound heavy metals (average CRPM 9.93E-05 and NCRPM 3.52). According to the sensitivity analysis performed using Monte-Carlo Simulation, it was also found that the output risk is most significantly influenced by the pollutant concentration followed by exposure duration and exposure time. The study reveals that Dhanbad city is not only a critically polluted area but also a highly hazardous and cancer-prone area due to the pollution arising from incessant coal mining activities and heavy vehicular movement. Given the paucity of data on exposure to VOCs in ambient air and their risk assessment pertaining to coal mining cities of India, our study provides useful information and insight for the regulatory and enforcing authorities to devise appropriate strategies for air pollution and health risk management in such cities.

Evaluation of the Impact of COVID-19 Restrictions on Air Pollution in Russia’s Largest Cities

Governments around the world took unprecedented measures, such as social distancing and the minimization of public/industrial activity, in response to the COVID-19 pandemic in 2020. This provided a unique chance to assess the relationships between key air pollutant emissions and track the reductions in these emissions in various countries during the lockdown. This study considers atmospheric air pollution in the 78 largest Russian cities (with populations over 250,000) in March–June of 2019–2021. This is the first such study for the largest cities in Russia. The initial data were the TROPOMI measurements (Sentinel-5P satellite) of such pollutants as carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), which are the main anthropogenic pollutants. The data were downloaded from the Google Earth Engine’s cloud-based geospatial data platform. This provided L3-level information for subsequent analysis. The TROPOMI data indicated a decrease in the atmospheric content of the air pollutants in the largest Russian cities during the lockdown compared to the pre-pandemic and post-pandemic periods. The reduced economic activity due to the COVID-19 pandemic had the greatest impact on NO2 concentrations. The average reduction was −30.7%, while the maximum reduction was found within Moscow city limits that existed before 01.07.2012 (−41% with respect to the 2019 level). For sulfur dioxide, the average decrease was only 7%, with a further drop in 2021 (almost 20% relative to 2019). For formaldehyde and carbon monoxide, there were no reductions during the 2020 lockdown period (99.4% and 100.9%, respectively, with respect to 2019). The identified impacts of the COVID-19 lockdown on NO2, SO2, HCHO, and CO NO2 concentrations in major Russian cities generally followed the patterns observed in other industrialized cities in China, India, Turkey, and European countries. The COVID-19 pandemic had a local impact on NO2 concentration reductions in major Russian cities. The differences leveled off over time, and the baseline pollution level for each pollutant was restored.

Study on the Temporal and Spatial Distribution of Air Pollutants in Typical Cities of China

The present study selects cities such as Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo, and Puyang along the Yellow River Basin in Henan Province. The data of six pollutants, such as PM2.5, PM10, SO2, CO, NO2, and O3, in various cities from 2019 to 2021, and the monthly primary pollutant data of seven cities in the past five years were collected through various channels. The air quality of the above seven cities was analyzed with the spatial-temporal distribution of pollutants as the research objective and geographic information system as the research tool. The results show that affected by the distribution of key pollution sources and meteorological conditions in the urban area, the PM2.5 concentration generally shows a zonal feature of decreasing from northwest to Southeast. The high-value area is located in the north and west of the integrated area of the seven cities, and the low-value area is located in the Southeast of the seven cities.

Driving forces and relationship between air pollution and economic growth based on EKC hypothesis and STIRPAT model: evidence from Henan Province, China.

The aim of this research is to analyze the main influencing factors and relationship between atmospheric environment and economic society. Using the panel data of 18 cities in Henan Province from 2006 to 2020, this paper employed some advanced econometric estimation included entropy method, extended environmental Kuznets curve (EKC) and STIRPAT model to conduct empirical estimations. The results show that most regions in Henan Province have verified the existence of the EKC hypothesis; and the peak of air pollution level in all cities of Henan Province generally occurred in around 2014. Multiple linear Ridge regression indicated that the positive driving forces of air pollution in most cities in Henan Province are industrial structure and population size; the negative driving forces are urbanization level, technical level and greening degree. Finally, we used the grey GM (1, 1) model to predict the atmospheric environment of Henan Province in 2025, 2030, 2035 and 2040. What should pay close attention to is that air pollution levels in northeastern and central Henan Province will continue to remain high.

Comparison of active and passive methods for atmospheric particulate matter collection: From case study to a useful biomonitoring tool

In this study we conducted air pollution monitoring using three different methods: active monitoring with the use of high volume aerosol sampler and biomonitoring with the use of lichens and spider webs. All of these monitoring tools were exposed to air pollution in Legnica city, a region of Cu-smelting in the SW Poland, which is well known for exceeding the environmental guidelines. Quantitative analysis was carried out for the particles collected by the three selected methods and concentrations of seven selected elements (Zn, Pb, Cu, Cd, Ni, As, Fe) were obtained. Concentrations found in lichens and in spider webs were directly compared and indicated significant differences between them, with higher amounts noted for spider webs. Then, in order to recognize the main pollution sources the principal component analysis was conducted and obtained results were compared. It resulted that spider webs and aerosol sampler, despite different mechanisms of accumulation, show similar sources of pollution – in this case – copper smelter. Additionally, the HYSPLIT trajectories and the correlations between metals in the aerosol samples also confirmed that this is the most probable source of pollution. This study can be considered innovative as these three air pollution monitoring methods were compared, which has never been conducted before, and their comparison gave satisfying results.

Analysis of Data on Air Pollutants in the City by Machine-Intelligent Methods Considering Climatic and Geographical Features

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora.

Sentinel satellite data monitoring of air pollutants with interpolation methods in Guayaquil, Ecuador

In Ecuador, there is a limitation on air quality monitoring due to the cost of monitoring networks. Although air quality monitoring stations are instruments for air measurement, they do not cover an entire city due to their scope. Satellite remote sensing is now an effective tool to study atmospheric pollutants and has been applied to continuously assess a region and overcome the limitations of fixed stations. Despite the application of satellite data for air quality monitoring, there are some limitations, such as measurement frequency, cloud cover and wide spatial resolution, which do not allow the assessment of air pollution in cities. Therefore, downscaling, applying interpolation methods, is essential for continuous air quality monitoring at smaller scales. For this research, Nitrogen Dioxide (NO2) data from the Sentinel-5 satellite percussor was used in the city of Guayaquil for January–December 2020, which is considered before, during and after the COVID-19 quarantine. This mid-size port city does not have a permanent monitoring network, which prevents us from knowing the air quality. Due to the limitation of pixel size, this study used satellite data to apply interpolation techniques and reduce pixels to assess air quality. Two categories of interpolation were selected: deterministic and stochastic. The empirical Bayesian kriging (EBK) interpolation obtained a R2 of 0.9546, which was superior to the other methods applied. Therefore, the EBK method had the best accuracy for tropospheric NO2 concentration. Finally, the method used in this research can help monitor air quality in cities lacking continuous monitoring networks, as the reduction of the pixel size gives us a better pattern of pollutants.

Mobility as Service: How Changing Travel use Habits will Affect the Transition of Cities to Climate Neutrality

Transportation contributes significantly to air pollution and carbon emissions in cities, negatively impacting human health and the environment. Mobility as a Service (MaaS) is considered one of the promising solutions to this problem, as it reduces the number of trips by private vehicles. MaaS integrates different modes of transportation such as buses, trains, bicycles, and cars into a single platform, which increases the attractiveness of shared transportation and micromobility tools for users and facilitates the shift from private cars to more environmentally friendly modes of transportation. MaaS brings tangible changes in transportation use behavior. Research on behavioral factors influencing MaaS use and their relationship to transportation habits is underdeveloped, especially in regions with high levels of private car use and low environmental awareness. This article presents the results of a survey aimed at identifying the factors that influence transportation user behavior and the policies needed to promote MaaS adoption. These data are then used to assess the impact of MaaS on cities’ efforts to achieve carbon neutrality. The survey was conducted in the city of Riga, the capital of Latvia, and analyzes factors such as necessary changes in infrastructure, service design, pricing, public awareness, and information efforts to reduce carbon emissions.

An episode-based assessment for the adverse effects of air mass trajectories on PM2.5 levels in Astana and Almaty, Kazakhstan

PM2.5 pollution is a severe problem in most cities in Kazakhstan and is a leading cause of premature deaths globally. This study examines the temporal variation of PM2.5 levels and the effect of air mass transportation dynamics on episodic air pollution in Kazakhstan's two largest cities. In 2021 annual PM2.5 concentrations in Astana (22.5 μg m−3) and Almaty (35.3 μg m−3) exceeded the annual WHO limit by 4.5 and 7.1 times, respectively. The daily limits surpassed 151 days in Astana and 217 days in Almaty. PM2.5 levels were highest in winter averaging 35.3 μg m−3 (Astana) and 76.0 μg m−3 (Almaty) over the heating season, with coal combustion as a main contributor. The HYSPLIT model was used to assess the impact of air mass trajectories on pollution episodes and clean air events. Cluster and concentration-weighted trajectory analyses showed that air pollution episodes in both cities were associated with the anticyclone-blocking effect. While both cities were exposed to the influence of long-range transported pollutants, Astana was more frequently impacted by distant sources. Given the transboundary nature of air pollution in both cities and the complex relationship between meteorology and emission sources, a regional integrated air quality improvement strategy is needed.

How can trees protect us from air pollution and urban heat? Associations and pathways at the neighborhood scale

With rapidly increasing urbanization, cities frequently suffer from the development of urban pollution and heat islands, the co-occurrence of which increases the vulnerability of cities and threatens public health. Greening practices, particularly the plantation of trees, have become an effective strategy for enhancing the quality of urban environments. However, extensive studies examining the effects of urban greening tend to focus on the singular contribution of trees to mitigating either urban air pollution or heat. In this study, the metropolis of Shanghai, China was selected as a study case to explore associations between particulate matters with aerodynamic diameters ≤10 μm (PM10) and heat mitigation by urban trees. During the summer months, we obtained hourly PM10 concentration and meteorological data from environmental monitoring stations in Shanghai, and land cover data were derived from 0.8-m-resolution GF-2 satellite images. Regression analysis indicated a negative non-linear logarithmic pattern between canopy cover and PM10 or heat indicators within neighborhoods. Mediation analysis confirmed the significant mediatory role of heat mitigation pathways, particularly extreme heat mitigation on PM10 reduction from urban trees. The proportion of indirect PM10mean mitigation effects of urban trees associated with reductions in average temperature, maximum temperature, extreme heat days, and extreme heat hours amounted to 27.11%, 27.59%, 30.61%, and 39.76%, respectively. Different results were obtained when using PM10max as the dependent variable, with only extreme heat events (EHEs) pathways being significant, whereas the proportion of indirect effects associated with extreme heat days and extreme heat hours accounted for 25.89% and 37.08%, respectively. Overall, mitigating extreme heat events makes a larger contribution to air purification than temperature regulation. Our findings enrich and extend the current understanding of associations between the air purification and cooling effects of urban trees, and highlight the potential co-benefits for effectively dealing with air pollution and urban heat in high-density cities.