Pollution In Urban Areas Research Articles

Determination of the sensing properties of the fluorescence-based sensor for atmospheric NO2 gas

Air pollution in urban areas poses a serious threat to human health and therefore the studies about the development of low cost and sensitive sensors to monitor the air quality with high spatial and low temporal resolution continue to be an extensive area in literature. In this study, oxime modified poly(4-(1-pyrenyl) styrene) (P(PySt)-NOX) probes were synthesized to use as a sensor to detect NO2 gas in ambient air. The structural characterization results showed that the probe was successfully synthesized. The sensitivity, selectivity, repeatability, and aging tests were performed during the study, and it was observed that P(PySt)-NOX loaded sensor is sensitive to NO2 for concentrations below 100 ppb. The selectivity measurements were performed against O3 and SO2 which are common interfering gases in ambient air, and it was shown that the sensor is selective to NO2. Additionally, according to the aging tests performed in laboratory for 23 days, it was observed that the sensor is stable in this time interval. The studies showed the sensor synthesized and designed in this study is suitable for NO2 concentration measurements in ambient air where the concentration levels of NO2 is below 100 ppb.

Understanding the multifaceted influence of urbanization, spectral indices, and air pollutants on land surface temperature variability in Hyderabad, India

Hyderabad, similar to many cities worldwide, has experienced accelerated urbanization, leading to increased surface temperature and air pollution. The present study investigated the relationship between land surface temperature (LST) and various air pollutants, urbanization, surface indices, and meteorological parameters, which are associated with Sustainable Development Goals (SDGs), especially Goal 13 (Climate Action) and Goal 11 (Sustainable Cities and Communities). The effect of air pollution on LST across seasons was also examined for five years (2018–2022). LST images collected from MODIS satellite. Data on air pollutants and meteorological parameters were collected from air quality monitoring stations. The results showed that PM2.5 exceeded the WHO guidelines. A weak or moderate relationship was observed between LST and individual air pollutants. However, the association significantly improved when all air pollutants were considered simultaneously. The relationship improved even further when additional parameters such as Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), road line density (RLD), and global population density (GPD), were also included. The resulting relationship was very strong (R2 > 0.80) during the summer and winter seasons. The study identified that NH3 had a prominent influence on LST during winter, while PM2.5 and NH3 exhibited a higher influence during summer. The research findings emphasized that LST variations were influenced by both air pollutants and meteorological conditions, along with urbanization factors represented by various parameters and indices. The research underscores the importance of considering these findings in future studies. It suggests the need to explore efficient approaches to address the combined problem of rising LST and concentration of air pollutants in urban areas.

Bioluminescence Inhibition Bioassay for Estimation of Snow Cover in Urbanised Areas within Boreal Forests of Krasnoyarsk City

It has been proposed that the level of air pollution in a city should be estimated based on the accumulation of pollutants in the snow cover of urban forests. This study presents a bioluminescence method for estimating the extent of snow cover pollution in the urbanised areas of boreal forests in Krasnoyarsk city. A bioluminescent assay involving NAD(P)H:FMN oxidoreductase (Red) and luciferase with luminous bacteria (BLuc) was employed to measure the concentrations of six heavy metals (As, Cd, Zn, Co, Hg, and Pb) in the snow cover. The tested snow samples demonstrated a correlation between the reduced activity of the enzyme system and variations in Cd concentration. Furthermore, the research indicated that the period of unfavourable meteorological conditions in Krasnoyarsk city resulted in a notable decline in the activity of the BLuc–Red enzyme system, which may be associated with elevated air pollution levels. This study underscores the potential of the bioluminescence method for monitoring environmental pollution in urban forested areas.

Efficient Mako Shark-Inspired Aerodynamic Design for Concept Car Bodies in Underground Road Tunnel Conditions.

The automotive industry continuously enhances vehicle design to meet the growing demand for more efficient vehicles. Computational design and numerical simulation are essential tools for developing concept cars with lower carbon emissions and reduced costs. Underground roads are proposed as an attractive alternative for reducing surface congestion, improving traffic flow, reducing travel times and minimizing noise pollution in urban areas, creating a quieter and more livable environment for residents. In this context, a concept car body design for underground tunnels was proposed, inspired by the mako shark shape due to its exceptional operational kinetic qualities. The proposed biomimetic-based method using computational fluid dynamics for engineering design includes an iterative process and car body optimization in terms of lift and drag performance. A mesh sensitivity and convergence analysis was performed in order to ensure the reliability of numerical results. The unique surface shape of the shark enabled remarkable aerodynamic performance for the concept car, achieving a drag coefficient value of 0.28. The addition of an aerodynamic diffuser improved downforce by reducing 58% of the lift coefficient to a final value of 0.02. Benchmark validation was carried out using reported results from sources available in the literature. The proposed biomimetic design process based on computational fluid modeling reduces the time and resources required to create new concept car models. This approach helps to achieve efficient automotive solutions with low aerodynamic drag for a low-carbon future.

Role of Law in Combatting Air Pollution

Air pollution in urban areas arises from multiple sources, which may vary with location to location and activity to activity of development. Anthropogenic activities such as rampant industrialization, exploitation and over consumption of natural resources, population explosion are major contributors of air pollution. Regulations and legislations adopted are enumerated. The Clean Air Act envisaged guidelines for industries regarding emission. It has been seen and observed that in spite several rules and regulations, not much has been achieved in controlling air pollution particularly in countries like India. India needs to make tough legislations The present paper is an effort to discuss various aspects of air pollution and control legislation in India emphasizing on the history, present scenario, gaps and drawbacks. It also presents legislative controls with judicial response to certain landmark judgments related to air pollution. The down sides related to enforcement mechanism for the effective implementation of environmental laws for air pollution control have been highlighted.

Optimized Design of Low Emission Zones in SUMO

Focusing on the critical challenge of air pollution in urban areas, primarily caused by vehicular emissions, this study proposes an innovative process for Low Emission Zones (LEZ) design within the Simulation of Urban Mobility (SUMO) framework. Our primary focus is on enhancing urban mobility through the strategic design of LEZs, while simultaneously maintaining or even improving emission levels. The novel aspect of the approach lies in the use of LEZs with minimal geometric boundaries, strategically designed to balance the reduction of CO2 emissions and the necessity of fluid urban transportation. LEZs are calculated using genetic algorithms that optimize a cost function balancing emissions and travel time while applying topological and specific constraints. Several experiments are simulated with SUMO to compare the efficiency of urban mobility under various LEZ configurations and different traffic demands. The results show the improvement of the approach in comparison with traditional LEZ design methodologies. The proposal not only preserves or even reduces the emission levels, but also actively improves urban mobility and traffic flow. This empirical evidence strongly supports the feasibility and effectiveness of the proposed solution in different urban scenarios. The design of the heuristic enables the possibility to create dynamic LEZs that may be changed depending on demand, weather, or any other varying conditions that affect traffic and emissions, preserving the mobility concerns of the users.

Impact of vegetation cover and land surface temperature on the seasonal tropospheric NO2 level variation from satellite observation

The Southeast Asian Region is one of the world’s regions that has historically had poor air quality conditions due to excess fossil fuel usage, coal combustion, and intensive deforestation activities. As the hub of economic activity, Indonesia’s Jakarta and Palembang cities have drawn significant attention from rural areas, leading to unrestrained urbanization in the past decade. Therefore, this study aimed to integrate the two urban centers of Indonesia, Jakarta and Palembang. It analyzed the variation of nitrogen dioxide (NO2) concentration, vegetation cover, and land surface temperature (LST) across all seasons from 2021 to 2023. We retrieved the tropospheric NO2 concentration data from the ozone monitoring instrument (OMI) aboard Aura Satellite, and obtained LST and the normalized difference vegetation index (NDVI) from Landsat 8 OLI images. We then used a Pearson correlation analysis to determine the relationship between the NO2 concentration, NDVI, and LST. The findings showed that Jakarta had higher levels of air pollution than Palembang, with rising LST and NO2 levels. During the study period, the mean NO2 concentration in Jakarta and Palembang was 3.25 × 1015 mol/cm2 and 0.87 × 1015 mol/cm2, respectively. While, the mean LST values in Jakarta and Palembang were 33.97 °C and 27.17 °C, respectively. The correlation results indicated a negative relationship between NO2 level and NDVI (r = −0.378, p < 0.01), while it obtained a positive association with LST (r = 0.421, p < 0.01). These results can be a chance for the authority to formulate a new strategy for mitigating air pollution, especially for NO2 pollutants in urban areas.

Utilizing Machine Learning for air pollution prediction, comprehensive impact assessment, and effective solutions in Kolkata, India

Escalating air pollution in urban areas is a matter of concern, and deteriorating air quality is having numerous impacts on human health and the environment. Kolkata is one of the most densely populated and highly polluted cities in India. The aim of this work is to predict the concentration of ambient PM2.5 using different air pollutants and meteorological parameters as predictor variables by using statistical and different Machine Learning techniques as well as to understand the influence of other air pollutants and meteorological factors in ambient PM2.5 prediction. Different advanced machine learning algorithms like Random Forest Regression, decision trees, k-nearest Neighbour, Support Vector Regression, Ridge Regression, Lasso Regression, and XGBoost have been used, and the results show that the XGBoost model exhibits higher linearity between predictions and observations, among other models. Moreover seasonal variation of the most influential factor for prediction of PM2.5 is also noticed during the analysis. This work adds to the broader comprehension of the convergence of environmental science, public health, and machine learning and it offers significant perspectives for sustainable urban planning and pollution control tactics in rapidly expanding metropolitan areas such as Kolkata.

Analysis of energy consumption efficiency and emissions according to urban driving of hybrid electric vehicles in Korea

Internal combustion engines contribute significantly to global warming and air pollution in urban areas with high population densities and abundant vehicular movement. Alternative energy vehicle, such as hybrid and electric vehicles, are being developed to address these concerns. However, a gradual transition to electrification while maintaining the existing fuel infrastructure appears more feasible than the rapid proliferation of battery-powered electric vehicles. Hybrid electric vehicles are recognized as an important means of improving vehicle fuel efficiency and reducing carbon emissions by introducing changes in driving modes to enhance energy consumption efficiency. Catalyst activation does not occur in cold starts, making it challenging to control exhaust emissions. In Korea, urban driving patterns characterized by speeds below 60 km/h often results in insufficient catalyst activation. Therefore, research on the characteristics of urban driving and cold-start conditions is essential. This study analyzed the urban driving characteristics of domestic hybrid vehicles reflecting urban conditions through chassis tests and examined the variations in the external temperature and engine contribution to assess the control capability of the hybrid system. Confirming that minimizing the engine preheating time during cold starts and efficiently utilizing energy stored in the motor can enhance fuel efficiency and reduce exhaust emission rates.

Electricity in Public Passengers’ Transportation and the Impact on Consumption in the National Energy System

Abstract Electric drive systems in mobility solutions are increasingly being used by the manufacturers in the global automotive industry. The European strategy to reduce greenhouse gas emissions to zero by 2050 is also transposed into Romania’s Energy Strategy. An important part of this strategy is the use of electricity in public passenger transport to significantly reduce pollution in urban areas. The rapid pace in terms of the renewal of conventional urban transport with modern electrified solutions requires, at the same time, an analysis of the pressure that this new consumer can exert on the national energy system.

Compact Pollution Control and Sound Indicator Device

Abstract: The increasing urbanisation of cities globally has intensified worries about car pollution, leading to substantial environmental damage and public health risks. Current Pollution Under Control (PUC) systems, which primarily focus on reducing exhaust emissions, often encounter usability problems and neglect the issue of noise pollution. This study presents a new and innovative approach called the Compact Pollution Control and Sound Indicator Device (CPCSID), which aims to overcome these limitations. The project includes state-of-the-art sensors capable of discriminating and measuring several exhaust gases generated by cars, including nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2). Additionally, it contains an integrated microphone to assess engine-generated noise levels. The LCD panel presents real-time data of pollution and noise, providing consumers with practical insights into the environmental effects of their car. In addition, our invention is integrated with a GSM module, which allows it to send SMS notifications to car owners. These warnings aim to promote environmentally aware behaviour and enable quick responses to reduce pollution. The small size and easy-to-use design make it suited for both individual car owners and fleet managers, providing a complete solution for reducing automobile pollution in urban areas.

Smart Car Parking System and Generation of Piezoelectric Energy

Abstract: In the past few years, parking has become a costly commodity in virtually every major urban region on the earth, and scarcity is a contributing factor to traffic jam and air pollution in urban areas. The most popular method to obtain a parking place is to do it manually, in which the drivers looks for a parking area either through experience or by luck. This method takes time and effort, and it may result in the worst-possible situation of not finding a parking place if vehicle is in a place with a high vehicle density. An effort is made here to park the vehicle as well as to find a parking space for the vehicle with a framework of Smart Parking System(SPS) which is based on the law of conservation of energy in we basically convert the elements from mechanical energy into electrical energy. This document Allow user to Automatically find a free site for parking at low cost without wasting much fuel and time.

The Impact of Environmental Gaseous Pollutants on the Cultivable Bacterial and Fungal Communities of the Aerobiome.

Understanding air microbial content, especially in highly polluted urban areas, is crucial for assessing its effect on human health and ecosystems. In this context, the impact of gaseous pollutants on the aerobiome remains inconclusive due to a lack of studies separating this factor from other contaminants or environmental factors. In this study, we aimed to experimentally assess the influence of contrasting concentrations of atmospheric gaseous pollutants as isolated variables on the composition of the aerobiome. Our study sites were contrasting Air Quality Index (AQI) sites of the Metropolitan Region of Chile, where nitric oxide (NO) was significantly lower at the low-AQI site than at the high-AQI site, while ozone (O3) was significantly higher. Cultivable aerobiome communities from the low-AQI site were exposed to their own pollutants or those from the high-AQI site and characterized using high-throughput sequencing (HTS), which allowed comparisons between the entire cultivable communities. The results showed increased alpha diversity in bacterial and fungal communities exposed to the high-AQI site compared to the low-AQI site. Beta diversity and compositional hierarchical clustering analyses revealed a clear separation based on NO and O3 concentrations. At the phylum level, four bacterial and three fungal phyla were identified, revealing an over-representation of Actinobacteriota and Basidiomycota in the samples transferred to the high-AQI site, while Proteobacteria were more abundant in the community maintained at the low-AQI site. At the functional level, bacterial imputed functions were over-represented only in samples maintained at the low-AQI site, while fungal functions were affected in both conditions. Overall, our results highlight the impact of NO and/or O3 on both taxonomic and functional compositions of the cultivable aerobiome. This study provides, for the first time, insights into the influence of contrasting pollutant gases on entire bacterial and fungal cultivable communities through a controlled environmental intervention.

Assessment of genotoxicity of air pollution in urban areas using an integrated model of passive biomonitoring

Atmospheric pollution is a major public health issue and has become increasingly critical for human health. Urban atmospheric pollution is typically assessed through physicochemical indicators aligned with environmental legislation parameters, providing data on air quality levels. While the effects of pollution on sensitive organisms serve as a warning for public health decision-makers, there remains a need to explore the interpretation of environmental data on pollutants. The use of species adapted to urban environments as sentinels enables continuous and integrated monitoring of environmental pollution implications on biological systems. In this study, we investigated the use of the plant species Tradescantia pallida as a biomonitor to evaluate the genotoxic effects of atmospheric pollution under diverse vehicular traffic conditions. T. pallida was strategically planted at the leading urban intersections in Uberlândia, Brazil. During COVID-19 pandemic lockdowns, we compared indicators such as physical, biological, and traffic data at different intersections in residential and commercial zones. The reduction in vehicular traffic highlighted the sensitivity of plant species to changes in air and soil pollutants. T. pallida showed bioaccumulation of heavy metals Cd and Cr in monitored areas with higher traffic levels. Additionally, we established a multiple linear regression model to estimate genotoxicity using the micronucleus test, with chromium concentration in the soil (X1) and particulate matter (PM) in the atmosphere (X2) identified as the primary independent variables. Our findings provide a comprehensive portrait of the impact of vehicular traffic changes on PM and offer valuable insights for refining parameters and models of Environmental Health Surveillance.

Dust Pollution in Urban Areas: Current Trends and Implications for Human Health and Outdoor Physical Activities

Dust Pollution in Urban Areas: Current Trends and Implications for Human Health and Outdoor Physical Activities

Unveiling the Spatial Dimensions of Light Pollution in Pakistan: An Emerging Environmental Challenge

The study aims to investigates about the growing and serious concern of light pollution as an emerging environmental challenge in Pakistan. The study also attempts to analyze the spatial dimensions of this issue, including its implications for biodiversity and public health. The study also attempts to analyze the spatial dimensions of this issue, including its implications for biodiversity and public health. In Pakistan, a notable research gap exists regarding the environmental impact of light pollution. Despite the absence of policies regulating artificial lighting, and a lack of public awareness on this issue, there is a pressing need for comprehensive research to address and mitigate the adverse effects of excessive artificial light in the country. This descriptive study employs spatial mapping techniques to investigate and highlight the pervasive issue of light pollution in Pakistan. Utilizing geographic information, the research analyzes and visually represents the spatial distribution of artificial lighting, providing a comprehensive understanding of the extent and intensity of light pollution across the country. It is found that the most light-polluted regions in Pakistan encompass the northern, eastern, and central sectors of Punjab, the southwestern and central areas of Sindh, the central part of Khyber Pakhtunkhwa, and the western region of Baluchistan. The study indicate that artificial sources of nightlight primarily cause light pollution in urban areas. This heightened light pollution coincides with major urban centers such as Rawalpindi, Lahore, Gujranwala, Faisalabad, Multan, Karachi, Hyderabad, Peshawar, and Quetta situated in these areas. Due to strong spatial effects, the light pollution extends from urban regions to their neighboring rural areas and depleting their dark spaces. Similarly, it is observed that, this strong spatial effect also causing light pollution alongside Indian border, substantiate the fact that light pollution from the urban regions of India creating light pollution in Pakistan. Furthermore, the study recommends comprehensive policy measures, including zoning regulations, public awareness campaigns, and technological solutions, to mitigate the adverse effects and spatial spread of light pollution.

A six-year measurement-based analysis of traffic-related particulate matter pollution in urban areas: the case of Warsaw, Poland (2016-2021)

This study used PM10 and PM2.5 measurements from the State Environmental Monitoring stations in Warsaw and its suburban areas. Analysis of variability characteristics at the traffic and urban background stations was carried out for 2016-2021. A six-year analysis (2016-2021) of air quality in Warsaw, Poland, focusing highlights the persistent impact of transportation on particulate matter concentrations. Comparing a city centre traffic station with urban background locations reveals consistently higher PM10 concentrations at the traffic station throughout the year, with an annual traffic-related increase of 12.6 μg/m³ (32%). PM2.5 concentrations at the traffic station are also consistently about 1.5 μg/m³ (7%) higher. For monthly averages, the highest PM10 concentrations at the traffic station were noted in March, which may be related to the resuspention of sand and salt left over from winter snow removalp rocesses. In the case of PM2.5, the typical annual cycle with maximum concentrations in winter and minimum concentrations in summer was not observed. Diurnal variability patterns show elevated PM10 concentrations at the traffic station from 8:00 a.m. to 10:00 p.m., attributed to the resuspension process. PM2.5 patterns exhibit a smaller amplitude at the traffic station, with nighttime accumulation due to inflow. This study emphasizes the lasting impact of transportation on air quality, providing insights into pollution control strategies in urban areas.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Modelling Consumers’ Preferences for Time-Slot Based Home Delivery of Goods Bought Online: An Empirical Study in Christchurch

Due to the remarkable growth in online retail sales in New Zealand, a large number of parcels are needed to be delivered to consumers’ doorsteps. Home deliveries in major New Zealand cities (e.g., Christchurch) typically occur between 9 a.m. and 6 p.m. on weekdays, when many home delivery attempts fail. This leads to adverse effects, such as vehicular traffic in residential areas and greater air pollution per parcel delivered. However, home deliveries outside of typical business hours (i.e., before 9 a.m. and after 5 p.m.) might be worthwhile to help subside the above issues. Therefore, this study investigated consumers’ preferences for receiving home deliveries during various times, such as early morning, morning, afternoon, late afternoon, and evening. The data used in this study were obtained via an online survey of 355 residents of Christchurch city. Non-parametric tests, namely the Friedman test, Wilcoxon signed-rank test, and ordinal logistic regression, were carried out to examine consumer preferences for the above time slots. The results showed that consumers preferred the late afternoon (3 p.m. to 6 p.m.) time slot the most for receiving home deliveries. It appeared that the off-peak delivery option is less likely to draw the desired consumer patronage and is thus less likely to assist in lowering the number of unsuccessful home deliveries, the transportation costs incurred by service providers, traffic congestion, and pollution in urban areas.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression.

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.