Air Quality In Urban Areas Research Articles

State of atmospheric pollution between vulnerabilities and criticalities from 2007 to 2022 and a projection in 2030 focused on the proliferation of gasoline sales in the city of N'Djamena

According to the World Health Organization (2008), 1.3 million annual deaths worldwide are attributable to air pollution, and that the latter is the cause of 16% of the total number of deaths. Among air pollutants, particles play an important role in worsening air quality in urban areas. The city of Ndjamena has recorded an increased proliferation of hydrocarbon stations since 2007 to the present day. The analysis of the vulnerability and criticality of pollutants from 2007 to 2022 and a projection to 2030 were made. For a response relating to the subject of our research, we chemically characterized two types of gasoline by following the recommended parameters. The result obtained following these analyses shows that street gasoline has a more or less negative result compared to gasoline produced, stored and sold in accordance with standards and regulations in Chad. We note that the analyzed contraband gasolines have negative results unlike other types of gasoline sold at the pump (some service stations) in terms of: - ASTM distillation, confirming the presence of significant heavy fractions, the Octane Index, presenting a minimum limit required by the standard for super 90 gasoline. Total sulfur, which reaches the maximum limit required and finally, Corrosion giving the maximum limit required by the standard in terms of corrosion. We also noted the presence of traces of water and unidentified solid debris at the bottom of most of our contraband samples.

Air pollution tolerance and metal accumulation potential of some plant species growing in educational institutions of Amritsar, India.

Poor air quality in urban areas increases the exposure of individuals to air pollutants. Hence, it becomes mandatory to grow such plant species that have more potential to tolerate air pollution and can aid in its mitigation. Air pollution tolerance index (APTI) and anticipated performance index (API) are two indices that help in scientific evaluation of plant species before recommending them for plantation. In this study, six plant species from three educational institutions of Amritsar city were screened for their tolerance and performance against air pollution as well as for their capability to act as accumulators of nine metals viz., aluminium (Al), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). On the basis of APTI, Cassia fistula (C. fistula) was categorized as a tolerant species while Alstonia scholaris (A. scholaris), Cascabela thevetia (C. thevetia), Monoon longifolium (M. longifolium), Pongamia pinnata (P. pinnata) and Syzygium cumini (S. cumini) were categorized as intermediately tolerant plant species. API results suggested that A. scholaris, C. fistula, M. longifolium, P. pinnata and S. cumini should be planted for air pollution mitigation. Geo-accumulation Index (Igeo) results showed that soil samples were moderately contaminated with three (Pb, Cu and Zn) metals. Bioaccumulation factor (BAF), for all metals among six plant species, was found to be less than one implying that these plants were absorbers of metals. Metal Accumulation Index (MAI) indicated that C. thevetia, C. fistula and P. pinnata exhibited relatively higher potential for metal accumulation.

Estimates of Air Pollutant Emissions by Vehicle Fleet in the State of Pará, Brazil: Impacts and Trends

Objective: aims to estimate emissions of the main atmospheric pollutants from vehicular sources in the state of Pará, to continue the environmental agenda of monitoring air quality. Theoretical Framework: The growing global concern about air quality in urban areas is driven by the environmental and public health impacts caused by air pollution. The concentration of pollutants from vehicle sources represents one of the main challenges, especially in densely populated regions such as the capital and other major cities in the state of Pará, where the automotive fleet has increased significantly. Method: The methodology used combined "bottom-up" and "top-down" methods to calculate emissions of carbon monoxide (CO), nitrogen oxides (NO) and total hydrocarbons (HC) for different vehicle categories and fuel types. Data on licensed vehicle fleets and fuel consumption obtained from DETRAN-PA and ANP were used. Results and Discussion: The results revealed that gasoline-powered vehicles are responsible for the majority of CO and HC emissions, while diesel vehicles emit the largest amounts of NO into the atmosphere. Motorcycles were identified as the largest source of emissions due to their larger number in the circulating fleet. Our results also highlight the importance of assessing and monitoring vehicle emissions to develop effective air pollution control strategies. Research Implications: The information obtained in this study is unprecedented for the study area, and can therefore support the formulation of environmental policies aimed at reducing emissions of atmospheric pollutants in the state of Pará, thus promoting an improvement in air quality and public health. Originality/Value: This paper makes an unprecedented contribution to national and international literature on environmental pollution in large urban centers. On the eve of the largest climate and environment conference on the planet (COP30) to be held in Pará, Amazonas, this research joins efforts in relation to the monitoring, forecasting and mitigation of air pollution, considered one of the main environmental problems of today.

Coupling MATSim and the PALM Model System—Large Scale Traffic and Emission Modeling with High-Resolution Computational Fluid Dynamics Dispersion Modeling

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NOx concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas.

Comparison of aerosol number size distribution and new particle formation in summer at alpine and urban regions in the Guanzhong Plain, Northwest China

Ultrafine particles play a crucial role in understanding climate change, mitigating adverse health effects, and developing strategies for air pollution control. However, the factors influencing the occurrence and development of new particle formation (NPF) events, as well as the underlying chemical mechanisms, remain inadequately explained. This study compared number concentrations and size distributions of atmospheric ultrafine particles at Xi'an (urban area) and the summit of Mt. Hua (alpine region) in summer to investigate the NPF mechanism and particle growth in both clean and polluted areas of the Guanzhong Plain. The average particle number concentration in Xi'an was significantly higher than that at Mt. Hua. The diurnal variation of total particle number concentration differed between Xi'an and Mt. Hua indicating a divergence in influencing factors. The size distributions in Xi'an varied across different timescales and weather conditions, whereas Mt. Hua exhibited little variation. This stability at Mt. Hua is attributed to its cleaner background atmosphere and the steady influx of aging particles with larger diameters transported from the free atmosphere. In both areas, geometric mean diameters (GMDs) were inversely proportional to particle number concentrations suggesting that increase in particle numbers were primarily due to the generation of smaller particles. The potential governing factors for NPF events differed somewhat between the urban and mountainous stations. In the urban area, intense local stationary and mobile emission sources promoted the growth of newly formed nanoparticles, with ozone-oxidized condensable vapors serving as key precursors. In contrast, at the mountainous station, NPF process were significantly influenced by anthropogenic precursors from long-range transport and locally emitted biogenic organics. The rapid increase in ultrafine particle concentrations primarily poses serious health risks and degrades air quality in urban areas, while also contributing to climate-related effects in alpine regions.

Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.

Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM10 dispersion in a 1.6 × 2 km2 urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution.

Improving Air Quality in Urban Areas by Enhancing Walking to School Programs: Addressing Mode Choice Barriers and Optimizing Walking Distance for Elementary School Students

Improving Air Quality in Urban Areas by Enhancing Walking to School Programs: Addressing Mode Choice Barriers and Optimizing Walking Distance for Elementary School Students

Assessment of the Atmospheric Deposition of Potentially Toxic Elements Using Moss Pleurozium schreberi in an Urban Area: The Perm (Perm Region, Russia) Case Study.

Assessment of air quality in urban areas is very important because pollutants affect both the environment and human health. In Perm (Russia), a moss biomonitoring method was used to assess the level of air pollution. The concentrations of 15 elements in 87 samples of moss Pleurozium schreberi in the city territory were determined using a direct mercury analyzer and an inductively coupled plasma atomic emission spectroscopy. Using factor and correlation analyses, the grouping of elements and their relationship with emission sources were established. The main sources of emissions of potentially toxic elements are the transportation (road and rail), metallurgical, and chemical industries. The level of atmospheric air pollution was assessed by calculating the environmental risk index, pollutant load index, and pollution coefficient. Based on the values of the pollution index, the level of atmospheric air pollution in Perm varies from unpolluted to highly polluted, with moderate environmental risk.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Study on main sources of aerosol pH and new methods for additional reduction of PM2.5 during winter severe pollution: Based on the PMF-GAS model

In recent years, the air quality in urban areas of China has been a significant improvement. However, further advancements in air quality remain challenging, particularly in addressing the persistent problem of severe particulate matter (PM) pollution in winter. In-depth research on the aerosol acidity (pH) is beneficial for analyzing the pollution characteristics of PM and providing support for the continuous improvement of PM pollution. In this study, the sources of water-soluble inorganic ions (WSIIs) of PM2.5 in Zhengzhou were analyzed based on the PMF-GAS method, and the contribution of different sources to aerosol acidity was further investigated in conjunction with the ISORROPIA-II thermodynamic model. The research results indicate that secondary aerosols are the main source, and controlling the emissions of secondary aerosols and gaseous precursors is a crucial measure for reducing PM2.5 concentrations. Furthermore, the study revealed that targeted reductions of major pollution sources across varying pH ranges could significantly lower PM2.5 concentrations. In the range of pH < 4, when the contribution of secondary aerosol source is 23.9%, PM2.5 concentrations can be reduced by 40.6%, resulting in an additional emission reduction of 5.9 μg m−3. In the range of 4 < pH < 6, PM2.5 can be reduced by 36.9% at a contribution of 16.5% from secondary aerosol source, achieving an additional emission reduction of 7.0 μg m−3. This research provides important theoretical support for gaining a deeper understanding of the sources and control of atmospheric pollution, offering strategies to expedite the achievement of PM2.5 standards and further mitigating its impacts on human health and the environment.

Enhancing Sustainable Thermal Comfort of Tropical Urban Buildings with Indoor Plants

Rapid urbanization exacerbates the urban heat island effect, raising local temperatures and endangering residents’ health and well-being. The decreasing green spaces resulting from urbanization necessitate global action focused on reducing heat island intensity and addressing heat stress. Urban green infrastructure (UGI) offers solutions for enhanced comfort and reduced pollution through passive methods. Various large-scale UGI projects have been implemented to regulate temperature and improve air quality in urban areas. More research on smaller green spaces is essential to improve the microclimate in space-constrained urban cities. This experimental study examines the thermal effectiveness of potted plants located on balconies of a mid-rise residential building in Chennai, India. The study aims to enlighten balcony greening’s role in reducing heat stress by monitoring temperature and humidity indoors and outdoors, with and without potted plants at similar solar radiation. Potted plants significantly lowered indoor air and surface temperatures by about 3 °C. Thus, balconies offer untapped potential for green interventions that are often unnoticed in tropical climates like India. The challenges in the installation and maintenance of UGI hinder the widespread adoption of UGI even though UGI positively influences residential well-being. The significant findings benefit urban planners and architects, enlightening strategies to enhance urban thermal comfort and mitigate heat stress through small-scale and cost-effective green interventions. This research contributes to sustainable urban development in tropical climates, aligning with several UN Sustainable Development Goals (SDGs), including SDGs 3, 7, 11, 13, and 15.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Integrating Passive Biomonitoring and Active Monitoring: Spider Web Silk and Portable Instruments for Air Quality in Urban Areas

Urban areas worldwide face significant challenges from increasing air pollution, posing health risks and environmental concerns. Lahore, a major city in Pakistan, is particularly affected by severe air pollution due to rapid industrial growth, high vehicle emissions, and various human activities. Traditional air quality monitoring methods, while effective, are often costly and complex, limiting their widespread use. This study investigates a dual monitoring system combining passive biomonitoring with spider web silk and active measurements using portable instruments to assess airborne pollutants in urban environments. Spider webs, collected from various sites in Lahore, were analyzed for heavy metals, while portable instruments simultaneously measured PM2.5, PM10, total volatile organic contents, formaldehyde, and carbon monoxide at the same locations. A spatial distribution analysis using GIS and statistical analysis revealed a pattern with significant changes as per land use in the urban environment related to anthropogenic activities. The Principal Component Analysis revealed three distinct clusters of pollutants origins consisting of: I) effluent drains and landfills, II) construction sites, and III) residential areas. The concentrations of Hg in spider silk (average ~ 2.66 mg/kg) were found to be significantly higher in the northeastern part of Lahore, with a similar trend observed in Cd, Cu, Ni, and As levels (average ~ 15.45, 102.87, 31.72, and 6.64, respectively). Several-fold changes in levels and spatial distribution of Pb (15.12 to 356.41 mg/kg) showed a higher concentration in the northern and northeastern parts of Lahore. The spatial variation pattern of Cr (average~ 57.04 mg/kg) registered a higher concentration in southwestern Lahore. Ambient air levels of measured pollutants followed almost similar patterns in spatial distribution. PM2.5 and PM10 measured higher levels (195 and 226 μg/m3, respectively) in the southwestern and northwestern areas, with a similar pattern of variation observed in TVOC (average ~ 0.14 μg/m3). The concentration of HCHO (average ~ 0.003 μg/m3) was higher in the southwest of Lahore. All measured pollutants registered higher values than air quality standards. Particulate matter is the most dominant pollutant contributing to air pollution (up to 20x higher than WHO guidelines). The findings support the use of a dual monitoring system, integrating passive spider web silk biomonitoring and active portable instruments, as a scalable and sustainable solution for air quality management. This approach holds potential for global application in diverse urban environments, with future research focusing on further validation and integration with advanced remote sensing technologies to enhance air quality monitoring and contribute to improved public health and environmental management worldwide. Hence, this research work points to the potential use of spider silk as a biomonitoring tool in combination with instrumental measurement of ambient air pollutants.

Source Apportionment of Air Quality Parameters and Noise Levels in the Industrial Zones of Blantyre City

The increase in industrial activities has raised concerns regarding air quality in urban areas within Malawi. To assess the source apportionment of air quality parameters (AQPs) and noise levels, concentrations of AQPs (CO, TSP, PM 2.5, PM10) and noise levels were monitored at 15 sites in Makata, Limbe, Maselema, Chirimba, and Maone during dry and wet seasons, respectively. Active mobile multi-gas monitors and a Dylos DC1100 PRO Laser Particle Counter (2018 model) were used to monitor AQPs, while Integrated Sound Level Meters were used to measure noise levels. Monitoring and analysis were guided by the World Health Organization (WHO) and Malawi Standards (MS). A Positive Matrix Factorization (PMF) model was used to determine source apportionment of AQPs, and matrix trajectories analysed air mass movement. In the wet season, the average concentration values of CO, TSP, PM10, and PM2.5 were 0.49 ± 0.65 mg/m3, 85.03 ± 62.18 µg/m3, 14.65 ± 8.13 µg/m3, and 11.52 ± 7.19 µg/m3, respectively. Dry season average concentration values increased to 1.31 ± 0.81 mg/m3, 99.86± 30.06 µg/m3, 24.35 ± 9.53 µg/m3, and 18.28 ± 7.14 µg/m3. Noise levels remained below public MS and WHO standards (85 dB). Positive correlations between AQPs and noise levels were observed, strengthening from weak in the dry season to moderately strong in the wet season. PMF analysis identified key factors influencing AQPs accumulation, emphasizing the need for periodic sampling to monitor seasonal pollution trends, considering potential impacts on public health and environmental sustainability. Further studies should look at factors affecting the dynamics of PMF in Blantyre City.

Real-time air quality monitoring based on locally developed unmanned aerial vehicle and low-cost smart electronic device

This article deals with the development of real-time air quality monitoring based on Unmanned Aerial Vehicles (UAVs) and low-cost Internet of Things (IoT) devices. This investigation aims to design and develop an UAV-based platform that can monitor a large number of air pollutants in real-time with high spatial and temporal resolution. The proposed environmental monitoring system consists of five main elements, namely the UAV, sensors, data storage module, programmable card, and IoT communication module. Estimated pollutants such as particulate matter (PM2.5) and toxic gases (carbon monoxide CO, nitrogen dioxide NO2, and carbon dioxide CO2) are detected by low-cost sensors. The ZigBee wireless protocol is used for communication between the PC and UAV. This work is carried out to assess the air quality in urban areas, given the heavy road traffic and the emissions of some companies. The data analyzed were collected from December 2, 2022 to January 3, 2023, in two major cities of Cameroon, Douala and Kribi. The periodic average values of the detected pollutants are 222 ± 22 μg/m3 and 85.7 ± 8.6 μg/m3 for PM2.5, 560.8 ± 1.0 ppm and 555.6 ± 1.0 ppm for CO2, 4.2 ± 0.2 ppm and 0.7 ± 0.1 ppm for NO2, and 27.6 ± 2.8 ppm and 4.5 ± 0.5 ppm for CO in Douala and Kribi respectively. This made it possible to have an air quality index (AQI) of 444.2 for Douala City and 171.3 for Kribi City. These high values indicate poor air quality during the measurement period.

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

AbstractAir pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25min and 3s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Gaussian process-based analysis of the nitrogen dioxide at Madrid Central Low Emission Zone

Abstract Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, $[NO_{2}]$. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation.

Does urban bus route assignment improve air quality?

Worldwide, one of the most important causes of mortality is air pollution. To solve this problem, governments have implemented policies to reduce on-road and industrial emissions. In this regard, the Barcelona city council and Transports Metropolitans de Barcelona (TMB) started implementing the Nova Xarxa de Bus (NXB) to redefine the bus network following the criteria of connectivity, efficiency, and rationality. This policy was implemented in seven phases from 2012 to 2018. In this context, this paper analyses the impact of this policy on Barcelona’s air quality using a dataset from 2008 to 2016. Using a difference-in-difference approach, we show that implementing these new routes increased air quality in Barcelona. Additionally, we show that pollution decreased in each phase analysed, especially in the air quality stations near the main roads. From our results, we can infer that an optimal bus route design can improve air quality in urban areas.