Air Pollution In Cities Research Articles

Relationships among six urban air pollutants and identification of pollution types – A case study of Chinese cities above prefecture level

Urban air pollution is caused by the interplay of urban development and natural conditions, but it's unclear which factor has a greater impact. Studying the link between air pollutants and identifying the types of pollution causes is crucial for understanding the pollution mechanisms and formulating efficient management measures. During the research period from 2015 to 2020, this study analyzed the relationships between six air pollutants, calculated the city capacity (CC) and natural condition (NC) scores of 285 cities with the help of the index system and entropy weight TOPSIS model, and employed Gaussian Mixture Model (GMM) and random forest regression model to identify air pollution types. The results show: During the study period, the concentrations of most pollutants decreased, with SO2 experiencing the largest decline, reflecting the significant effectiveness of China's recent atmospheric pollution control efforts. In terms of spatial distribution, the North China Plain (NCP) and the Loess Plateau (LP) remain focal areas of pollution. The correlation analysis results indicate a significant positive correlation among all air pollutants except O3, underscoring the necessity of coordinated pollution control efforts, but the complexity of O3 pollution should not be overlooked. High-value agglomerations of CC scores calculated by the model are predominantly located in the eastern coastal region, while NC scores exhibit two high-value agglomerations in the north and south. Utilizing the GMM, the 285 cities were classified into three categories, and the random forest regression model was employed to identify them as CC dominate, NC dominate, and intermediate types. This paper proposes a novel approach for categorizing city air pollution types, aiming to tailor prevention measures and foster sustainable urban development.

DeepFogAQ: A fog-assisted decentralized air quality prediction and event detection system

Air pollution is a pervasive environmental and public health concern, prompting the imperative development of predictive systems to facilitate proactive interventions. The complexity of predicting air pollution arises from intricate factors, including the accumulation of pollutants, traffic dynamics, and industrial emissions. Traditional methodologies, reliant on historical or real-time data analysis, often encounter limitations in providing comprehensive and accurate solutions to this multifaceted problem. To address the existing problem, we propose a novel fog-assisted decentralized air quality prediction and event detection system (DeepFogAQ) for managing air pollution of future cities. We integrate Deep Learning (DL), Fog Computing (FC), Complex Event Processing (CEP), and virtualization technologies within the architecture of DeepFogAQ. Specifically, for predicting pollutant concentrations, we employ Transformers, CNN-LSTM, GRU, and RFR models. Additionally, we construct Fog and Cloud layers based on container-based virtualization technology. To demonstrate the feasibility of the system, the developed ML/DL models were run on DeepFogAQ and alarm levels for future air quality were derived. In this way, both the success of the prediction models and the validity of the architecture were ensured. Experimental results showed that Transformers is the most successful model in air quality prediction and event detection. As a result, the proposed DeepFogAQ architecture has the potential to offer a powerful alternative to decision-makers to solve the air pollution problem with its decentralized, scalable, and fault-tolerant structure.

Short-term and long-term effects of exposure to PM10

IntroductionA dust storm is a harmful event in the environment and human health resulting from certain chemicals in the air that transcend predefined levels. Developing countries, such as Iran, suffer from this basic threat (dust particles) to health. The purpose of the research was to evaluate the effects of PM10 particulate matter on mortality related to cardiovascular diseases (CVD) in the year 2022–2023 in Andimeshk City, with the AirQ + programmed. Material and methodThe recommended approach for evaluating effects on health, as recognized by the world health organization (WHO), uses the Air Quality Health Impact Assessment Software (Air Q 2.2.3). The polluted air information required for model runs was obtained from the Environmental Department of Khuzestan. The Khuzestan Meteorological Organization gave information on the levels of particulate matter for both dusty and non-dusty days. The mortality numbers came from the Iranian Statistics Centre and the Health Ministry. The data was collected in a volumetric manner for the year 2022–2023. ResultThe lowest PM10 concentration reported in a year in Andimeshk city belonged to January 9 (14.19 μg/m3), while the most polluted day of the year was November 6 (5634.61 μg/m3). The total monthly concentration of PM10 for April, May, June, July, August, September, October, November, December, January, and February respectively, 2917.9, 3227.82, 3175.89, 2966.24, 3520.46, 3393.1, 4671.07, 13121.26, 3042.63, 1851.67, 2627.52, and 6758.12 μg/m3. ConclusionThe results of this study showed that the studied area is a polluted city in terms of the concentration of dust particles. So, during a year, only 8 days, PM10 concentration did not exceed the standard. One of the main reasons for air pollution in Andimshek city is the neighborhood of Khuzestan province with Iraq and Saudi Arabia, which have dry areas and dust storms.

Spatial modeling of air pollution in Kharkiv city


 
 
 One of the most pressing problems facing cities, which negatively affects public health. There is a global practice of online monitoring of the content of pollutants in the air of settlements and informing the population about dangerous periods of increased pollution concentrations for certain categories of diseases and the population as a whole. It is also likely that one of the factors of pollution spread is the landscape differentiation of the city’s territory. The purpose of the study presented in this article is to develop statistical and cartographic models of the spatial distribution of air pollutants within the city of Kharkiv and to establish the relationship between the degree of air pollution and types of urban landscapes. Methods. Based on the analysis of the main methodological approaches and tasks of cartographic modeling of the spatial distribution of air pollution, the study substantiates the choice of the modeling method in this study; the features of a large city as an object of cartographic modeling of air pollution are assessed. Results. Based on the results of long-term monitoring by the Kharkiv Regional Center for Hydrometeorology, a statistical analysis of the results of the maximum and average content of pollutants in the atmosphere of Kharkiv was made and the spatial distribution of air pollution in Kharkiv (including (dust, carbon monoxide, nitrogen dioxide, sulfur dioxide, formaldehyde) was modeled, and patterns of pollution distribution were identified according to landscape types. Conclusions. In general, the spatial analysis of pollutant concentrations in the territories of Kharkiv showed that, in terms of maximum concentrations, the southeastern part of Kharkiv is most affected by carbon monoxide, nitrogen dioxide, and formaldehyde. The reason for this is the large area of industrial facilities. The northwestern part of the city has the highest concentrations of dust and sulfur dioxide. There is a lowering of the relief in the floodplain type of landscape and dense development. Intensive traffic and lowered relief (floodplain landscape) cause high average concentrations of dust, carbon monoxide, and sulfur dioxide in the southern part of the city. Nitrogen dioxide concentrations exceed the TLK in the northwest. The reason is the lowering of the relief on the outskirts of the city (floodplain landscape). The central part of the city has a high average concentration of formaldehyde. In general, a pattern of high concentrations of pollutants was found on the floodplains of the Lopan and Kharkiv rivers, with the lowest concentrations confined to watershed types of terrain between the Kharkiv and Lopan rivers and the Udy and Lopan rivers.
 
 

Observational study of major air pollutants over urban Romania in 2020 in comparison with 2019

This study aims to characterize urban air pollution in 33 cities across Romania in 2019 and 2020. The global COVID-19 pandemic that started in mid-March 2020 provided us with a unique opportunity to find out what would be the possible variation range of major air pollutants in Romanian urban areas under various strengths of anthropogenic activity. The time-series of atmospheric main pollutant mass concentrations, particulates PM10 and PM2.5 and gaseous species NO2, CO, SO2, O3, were statistically analyzed for each city for the representative month of each season in 2019 and 2020 and then used to estimate the pollutant impact on the surrounding regions. Results show significant decreases for most pollutants in most cities especially in spring of 2020, during the hard lockdown period. Maps of interpolated mass concentrations reveal regional significant differences with pollutant-specific hot- and cold-spots in Romania. This work constitutes a good database to characterize de urban air pollution in Romanian major cities and the spatial distribution of their impact around.

Speed limits and their effect on air pollution in Mexico City: A quasi-experimental study

Speed limits are an evidence-based intervention to prevent traffic collisions and deaths, yet their impact on air pollution in cities is understudied. The objective of this study was to investigate the association between lower speed limits and air pollution. We leverage the introduction of a new road safety policy in Mexico City in December 2015 which lowered speed limits, increased fines, and installed speed radars to enforce compliance. We tested whether the policy had an impact on particulate matter (PM2.5) and nitrogen dioxide (NO2) at the city level, and whether air-quality monitoring stations' proximity to speed radars moderated this effect due to more acceleration and deceleration around radars. NO2 and PM2.5 concentrations from January 2014 to December 2018 were obtained from the National System of Air Quality Information. Air-quality monitoring stations were classified as in close-proximity or far-from-speed radars. Interrupted time series analyses were conducted for each outcome separately, using linear mixed models and adjusting for seasonality and time-varying confounders: registered vehicles, temperature, wind-speed and relative humidity. The results suggest improvement in both contaminants after the speed limits policy. For NO2, the pre-policy trend was flat, while the post-policy trend showed a decline in concentrations of 0.04 ppb/week. For PM2.5, concentrations were increasing pre-policy by 0.08 μg/m3 per week, then this trend flattened in the post-policy period to a weekly, non-significant, increase of 0.03 μg/m3 (p = 0.08). Air-quality monitors' proximity to speed radars did not moderate the effect of the policy on either of the pollutants. In conclusion, the speed limits policy implemented in Mexico City in 2015 was associated with improvements in air pollution.

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations

AbstractThe rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper planning of future activities. In this manner, this study considers Istanbul, a province with a high concentration of industry, population, and vehicle traffic. Particulate matter (PM), one of the most basic air pollutants, is stated to contain microscopic solids or liquid droplets that are small enough to be inhaled and cause serious health problems. Thus, it is recommended to apply discrete wavelet transform (DWT) and deep learning method long short‐term memory (LSTM) as a hybrid model to predict the concentration of PM10. Using the mentioned methods, they can predict air pollution to have been developed within the scope of this study. Furthermore, the hybrid approach with LSTM by selecting the most appropriate discrete wavelet type emphasizes the difference of this study from the existing literature. The ability of these developed methods to make successful future predictions helps institutions and organizations that can take precautions on the subject to take action at the right time; in addition, the deep learning methods used contribute to the development of sustainable smart environmental systems. In today's environment when air pollution is increasing and threatening human health, any precaution that can be taken would improve the quality of life for all living things, reduce health issues and deaths caused by air pollution, and thus raise the degree of well‐being. These findings might offer a reliable scientific evidence for Istanbul City's air pollution management, which can serve as an example for other regions.

Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO2 concentrations in Accra, Ghana.

Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.

Studies on the Use of Unconventional Transport Vehicles and Fuels in the Island of Crete, Greece

Several alternative vehicles and zero-carbon fuels have been developed and used to mitigate climate change, reduce the air pollution in cities and the dependence on oil resources. Their use is necessary for achieving the net-zero emissions target in the next decades. Among these vehicles are electric vehicles and vehicles with ICEs using electricity, hydrogen and bio-fuels of organic origin. The possibility of using alternative vehicles and fuels in Crete, Greece for de-carbonization of vehicles transportation has been examined. The use of electric vehicles equipped with re-chargeable electric batteries, electric vehicles equipped with fuel cells using hydrogen and vehicles with internal combustion engines using bio-fuels has been studied. The required electricity for re-charging electric batteries and for electrolytic hydrogen production can be generated from the abundant local solar and wind energy resources. Taking into account the maturity and the cost-effectiveness of the solar-PV technology as well as the wind turbine’s technology in Crete the use of electric vehicles powered by green electricity is prioritized for the green transition of the transportation sector. Bio-fuels which are used in conventional vehicles are not produced in the island. Each type of the abovementioned vehicles and fuels has several advantages and drawbacks which can accelerate or delay their use in Crete. It has been found that the green transition of vehicles transportation in the island has positive and negative economic impacts to all stakeholders. The results could be useful to policy makers, local authorities and to local stakeholders while they can assist in the development of a plan for de-carbonizing the island by 2050.

Identification of Causes of Air Pollution in a Specific Industrial Part of the Czech City of Ostrava in Central Europe

This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to identify the sources of air pollution in the area, particularly in locations where the contribution of different sources to concentrations of pollutants of concern has not been elucidated yet. In this study, source apportionment in the vicinity of the industrial hot spot was performed by statistically evaluating measured pollutant concentrations as a function of meteorological variables and using the Positive Matrix Factorization (PMF) receptor model. A significant methodological innovation and improvement over previous assessments was the higher monitoring resolution of benzo[a]pyrene, with samples collected at three-hour intervals instead of the standard 24 h collection period. The key findings indicate that in the cold part of the year, secondary particles—specifically sulfates and ammonium nitrates—were responsible for the most significant portion of PM10 air pollution throughout the area of interest. The contribution of these particles ranged from one-third to two-fifths of the total concentration, except at the industrial site of Ostrava–Radvanice (TORE), where they accounted for approximately one-fifth of the measured pollution concentration level. Emissions from individual household heating were identified as the main source of this type of pollution. With regards to benzo[a]pyrene air pollution, this study found that in the whole area of interest, except for the Ostrava–Radvanice site, it mainly originated from individual heating with coal (90%). In contrast, at the Ostrava–Radvanice site, two-thirds of the benzo[a]pyrene pollution came from the premises of Liberty Ostrava a.s., primarily from coke production, and less than one-third came from local domestic heating. This study also determined the spatial extent of the occurrence of extremely high benzo[a]pyrene concentrations (above 5 ng/m3), which are estimated to affect nearly 10,000 inhabitants. The results confirm that the data from the TORE station are only representative of its immediate surroundings and are not applicable to the assessment of air quality and causes of air pollution in the whole city of Ostrava or the urban area of Ostrava–Radvanice as a whole. After years of research, these findings provide the Ministry of the Environment of the Czech Republic and the government with an accurate basis for implementing measures to address the identified pollution sources in the area of interest. The success of this study was made possible by the adoption of a more detailed sampling approach, which involved a resolution of 3 h instead of 24 h. This methodological improvement is a significant finding and will be useful for future source apportionment studies.

Assessment of Secondary Sulfate Aqueous-Phase Formation Pathways in the Tropical Island City of Haikou: A Chemical Kinetic Perspective.

Sulfate (SO42-) is an essential chemical species in atmospheric aerosols and plays an influential role in their physical-chemical characteristics. The mechanisms of secondary SO42- aerosol have been intensively studied in air-polluted cities. However, few studies have focused on cities with good air quality. One-year PM2.5 samples were collected in the tropical island city of Haikou, and water-soluble inorganic ions, as well as water-soluble Fe and Mn, were analyzed. The results showed that non-sea-salt SO42- (nss-SO42-) was the dominant species of water-soluble inorganic ions, accounting for 40-57% of the total water-soluble inorganic ions in PM2.5 in Haikou. The S(IV)+H2O2 pathway was the main formation pathway for secondary SO42- in wintertime in Haikou, contributing to 57% of secondary SO42- formation. By contrast, 54% of secondary SO42- was produced by the S(IV)+Fe×Mn pathway in summer. In spring and autumn, the S(IV)+H2O2, S(IV)+Fe×Mn, and S(IV)+NO2 pathways contributed equally to secondary SO42- formation. The ionic strength was the controlling parameter for the S(IV)+NO2 pathway, while pH was identified as a key factor that mediates the S(IV)+H2O2 and S(IV)+Fe×Mn pathways to produce secondary SO42-. This study contributes to our understanding of secondary SO42- production under low PM2.5 concentrations but high SO42- percentages.

Spatiotemporal patterns and drivers of the urban air pollution island effect for 2273 cities in China

Air pollution levels tend to be higher in urban areas than in surrounding rural areas, and this air pollution has a negative effect on human health. However, the spatiotemporal patterns of urban–rural air pollution differences and the determinants of these differences remain unclear. Here, we calculate the Urban Air Pollution Island (UAPI) intensity for PM2.5 and PM10 on a monthly, seasonal, and annual scale for 2273 cities in China from 2000 to 2020. Subsequently, we analyze the influence of urban characteristics using a combined approach of a two-way fixed effects model and a spatial Durbin model. Results show a strong downward trend in the UAPI intensity since 2013, with reductions ranging from 42 % to 61 % until 2020, for both pollutants and in summer as well as winter. Consistently, the proportion of the cities experiencing the UAPI phenomenon decreased from 94.5 % to 77.3 % for both PM2.5 and PM10. We find a significant influence of urban morphology on UAPI. Specifically, urban sprawl, polycentric development, and an increase in urban green spaces are associated with a reduction in UAPI, while dense urban areas intensify it. Our study also reveals a robust inverted U-shaped relationship between stages of economic development and UAPI. Moreover, economic development and air pollution itself show spillover effects that oppose their direct impacts. These results suggest that urban and regional planning and more ambitious climate change mitigation policies could be more effective strategies for mitigating air pollution in cities than end-of-pipe control.

AeroSense Monitor Integrasi Sensor DHT11 dan MQ135 untuk Pemantauan Kualitas Udara Berbasis Arduino Uno

Clean air is a basic necessity, but air pollution in Indonesia's major cities has increased significantly. The lack of air quality monitoring facilities in certain areas leads to a lack of public awareness of air pollution levels, which can adversely affect health. As a solution, the development of AeroSenseMonitor, an Arduino Uno-based air quality monitoring tool with DHT11 and MQ135 sensors, is proposed. This tool is expected to provide accurate information on air quality, enabling people to take preventive measures and protect their health. By raising awareness of the importance of clean air, it is hoped that the AeroSenseMonitor can be the first step in addressing the negative impacts of air pollution in Indonesia.

Four years of National Clean Air Programme (NCAP) in Indian cities: Assessment of the impact on surface ozone during the period 2018–2022

There is a significant increase in ozone at the surface and troposphere due to growing population, industrialisation and urbanisation. The initiation of National Clean Air Programme (NCAP) in 2019 marked a turning point in addressing air pollution in Indian cities. The Central Pollution Control Board (CPCB) ground-based measurements show a reduction in number of days with continuous exposure to 8 h surface ozone (MDA-8) exceeding 100 ppb since the implementation of NCAP. For instance, cities such as Visakhapatnam and Tirupati reported zero days of MDA-8 ozone surpassing 100 ppb in 2022. Also, a substantial reduction is observed in the frequency of MDA-8 ozone exceeding the 100 ppb threshold at other stations. The NO2 and PM2.5 measurements from CPCB show a decreasing trend at most stations, whereas satellite-based HCHO and NO2 measurements show negative (0–0.004 mol m−2 month−1) and positive (0–0.02 m−2 month−1) trends, respectively, during the period of 2019–2022. Therefore, although the implementation of NCAP is oriented towards reducing PM10 concentrations, it is also proven to be effective in curbing ozone pollution in most cities of India. This study, therefore, suggests to continue the efforts of NCAP and to implement tailored regulations for reducing ozone pollution in cities with high pollution.

Enhanced water resistance mechanism in Ag-Hollandite for catalytic ozone decomposition

Catalytic ozone (O3) decomposition at ambient temperature is an efficient method to mitigate O3 pollution. However, practical application is hindered by the poor water resistance of catalysts. Herein, Ag-Hollandite (Ag-HMO) with varying Ag+ content was synthesized. Catalysts with more Ag+ exhibited improved efficiency and water-resistance, with the optimal one maintaining 98% O3 conversion at 70% relative humidity (RH) within 8 h. Physicochemical characterizations revealed that Ag+ had entered the tunnel of OMS-2, facilitating oxygen species removal. Notably, enhanced H2O desorption and the complete inhibition of chemisorbed water formation on Ag-HMO were the primary reasons for its high-efficiency O3 conversion across a wide humidity range. The underlying mechanism arises from the charge redistribution induced by the Ag-O interaction within the tunnel, which reduces acidity and modulates hydrophilicity. This study aims to contribute insights for designing catalysts with higher water-resistance.

Causes Investigation of PM2.5 and O3 Complex Pollution in a Typical Coastal City in the Bohai Bay Region of China in Autumn: Based on One-Month Continuous Intensive Observation and Model Simulation

In order to investigate the causes of complex air pollution in coastal cities in the Bohai Rim of China, a one-month intensive field observation combined with model simulation was carried out in a typical city named Dongying in September 2022. The results showed that two PM2.5 and O3 complex pollution episodes occurred in Dongying in the observation period, with the primary pollutant being O3. Atmospheric photochemical reactions occurring under unfavorable meteorological conditions led to the production of O3 while at the same time facilitating the generation of nitrate, sulfate, and other secondary components of PM2.5 by enhancing the atmospheric oxidizing capacity, which promoted the formation of complex air pollution. It was worth noting that in the context of high pollutants emission, the occurrence of complex air pollution was more sensitive to the variation in meteorological conditions than to the change in pollutants emission. To continuously improve air quality and protect human health in Dongying, it is recommended that an effective regional joint air pollution prevention and control mechanism with neighboring cities should be established in the premise of effective local pollutants reduction, and special attention should be paid to the adverse effect of the air mass transportation from Bohai Bay.

In the line of fire: Analyzing burning impacts on air pollution and air quality in an Amazonian city, Brazil

Brazil is grappling with escalating wildfire incidents, particularly in the ecotone region of Cerrado-Amazon, attributed to climate change and anthropogenic factors. These wildfires significantly impact air quality, posing health and environmental risks to the population. This study aimed to assess the influence of wildfires on air quality in Sinop, a city located in Northern Mato Grosso, Brazil. The primary focus was on understanding the behavior of air pollutants during and after wildfire events. Air quality data were obtained from modeling dataset, while meteorological data from ground-based monitoring, and wildfire data from the National Institute of Spatial Research of Brazil. Statistical analyses were employed to investigate pollutant variations and their relationships with meteorological parameters and wildfire intensity indicators. The study revealed a significant impact of wildfires on pollutant concentrations, with ozone levels rising immediately after fires and remaining elevated for several days. The particulate matter (PM2.5 and PM10) concentrations increased a few days after fire events. Fire radiative power (FRP) correlated with O3 levels, suggesting it is an intensity indicator. Temperature exhibited a consistent positive correlation with all pollutants, highlighting its role in pollutant dynamics. The findings underscore the multifaceted relationship between wildfires, meteorological conditions, and pollutant concentrations pointing to the importance of holistic air quality management strategies in fire-prone regions. Such studies are crucial for guiding environmental policies, safeguarding public health, and advancing scientific understanding in the face of escalating wildfire challenges.

Impact of climatic factors on air pollution and human health in the Lucknow city of Uttar Pradesh, India

Urban air pollution is a complex and ever-changing blend of gaseous pollutants, particulate matter and land surface temperature. This multifaceted mixture experiences daily and seasonal fluctuations influenced by anthropogenic activities, land-cover transformation, and climatic conditions. This study investigates the impact of climatic factors such as temperature, precipitation, and relative humidity on air pollution and human health in Lucknow city of India. NASA's open data site (Data Access Viewer (DAV) - NASA POWER) was used to gather information on the earth's skin temperature, 2-meter temperature, precipitation, and relative humidity. Data on air pollutants like PM10, SO2, NO2, and the Air Quality Index across seven locations has been collected from the government pollution control board. The present research examines the correlation between these climatic factors and pollutants, such as particulate matter and sulphur dioxide, as well as the relationship between temperature and relative humidity with the incidence of respiratory and cardiovascular diseases in the study area. This research shows a negative correlation between pollutants and precipitation, temperature at 2m, relative humidity and earth skin temperature, respectively. On the other hand, it shows a positive correlation between precipitation and disease. Thus, this paper will provide valuable insights into the potential impacts of climate change on air pollution and human health in Lucknow and similar urban areas.

Situation Model of the Transport, Transport Emissions and Meteorological Conditions

Air pollution in cities and the possibilities of reducing this pollution represent one of the most important factors that today’s society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for predicting changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transportation more efficiently with environmental protection in mind.

The economic situation and ways to solve the environmental problems in the city of Ust-Kut

&lt;p&gt;The Ust-Kutsky district and its center, the Ust-Kut city, have become the fastest growing in the Irkutsk region of Russia in the last decade. Ust-Kut is a very important settlement for the economic development of the northern territories of the Irkutsk region and the southern territories of the Republic of Sakha (Yakutia). The city is a key transport hub, where various types of transport routes converge: railway, aviation, automobile, river, and product pipeline. There are deposits of various minerals near Ust-Kut. The timber mining and timber-processing industries are represented here as much as possible. On the other hand, Ust-Kut is one of the ten most air-polluted cities in the Irkutsk region. Most of the air pollution consists of substances formed as a result of fuel combustion. The unfavorable air regime of the city is influenced by climatic conditions: relatively weak winds and temperature inversions in winter. The oil and gas deposits located nearby may help solve the problem of air pollution by gasification of Ust-Kut. In addition, water and soil pollution have been noted in the city due to technological problems of wastewater disposal, garbage collection, and recycling, as well as certain types of industrial waste processing. Therefore, the main task of the sustainable development of the city is to solve its environmental problems. Ust-Kut's environmental problems are solved at the expense of financial resources from government programs, which are not sufficient. But the tax on mining is completely federal, as these funds do not remain in the territory. Through this research, we have searched for ways to achieve a balance between economic development and environmental well-being in Ust-Kut. Specifically, we have explored additional types of economic activities that are safe for nature and public health. For Ust-Kut, located in the north of Eastern Siberia, it is important to increase the area of green spaces within the central part of the city. The development of a system of parks and squares involves their improvement and the formation of a connection into a single natural framework through linear landscaping objects and connections with urban forest sites. The use of sanitary protection zones around enterprises that emit into the environment will contribute to the formation of a comfortable environment and expand landscaping opportunities.&lt;/p&gt;