Factors Influencing the Spatial Variability of Air Temperature Urban Heat Island Intensity in Chinese Cities

Few studies have investigated the spatial patterns of the air temperature urban heat island(AUHI)and its controlling factors.In this study,the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers.A total of 355 urban clusters were used.We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation.The results show that the midday AUHI is negatively correlated with climate wetness(humid:0.34 K;semi-humid:0.50 K;semi-arid:0.73 K).The annual mean midnight AUHI does not show discernible spatial patterns,but is generally stronger than the midday AUHI.The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid(0.32±0.09 K)and the semi-arid(0.36±0.11 K)climate zones.The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones.The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations.A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Outlier Detection of Air Quality for Two Indian Urban Cities Using Functional Data Analysis

Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.

Effects of air damping on quality factors of different probes in tapping mode atomic force microscopy

The AFM probe in tapping mode is a continuous process of energy dissipation,from moving away from to intermittent contact with the sample surfaces.At present,studies regarding the energy dissipation mechanism of this continuous process have only been reported sporadically,and there are no systematic explanations or experimental verifications of the energy dissipation mechanism in each stage of the continuous process.The quality factors can be used to characterize the energy dissipation in TM-AFM systems.In this study,the vibration model of the microcantilever beam was established,coupling the vibration and damping effects of the microcantilever beam.The quality factor of the vibrating microcantilever beam under damping was derived,and the air viscous damping when the probe is away from the sample and the air squeeze film damping when the probe is close to the sample were calculated.In addition,the mechanism of the damping effects of different shapes of probes at different tip–sample distances was analyzed.The accuracy of the theoretical simplified model was verified using both experimental and simulation methods.A clearer understanding of the kinetic characteristics and damping mechanism of the TM-AFM was achieved by examining the air damping dissipation mechanism of AFM probes in the tapping mode,which was very important for improving both the quality factor and the imaging quality of the TM-AFM system.This study’s research findings also provided theoretical references and experimental methods for the future study of the energy dissipation mechanism of micro-nano-electromechanical systems.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Impact of the Inlet Flow Angle and Outlet Placement on the Indoor Air Quality

This study aims to optimize the influence of the inlet inclination angle on the Indoor Air Quality(IAQ),heat,and temperature distribution in mixed convection within a two-dimensional square cavityfilled with an air-CO_(2)mixture.The air-CO_(2)mixture enters the cavity through two inlet openings positioned at the top wall,which is set at the ambient temperature(TC).Three values of the Reynolds numbers,ranging from 1000 to 2000,are considered,while the Prandtl number is kept constant(Pr=0.71).The temperature distribution and streamlines are shown for Rayleigh number(Ra)equal to 104,three inlet inclination anglesϕ(0,π/6 andπ/4)and three CO_(2)concentrations values(1500,2500,3500 ppm)applied at both hot vertical walls(maintained at a constant temperature TH).Afinite volume method is used under the assumption of two-dimensional laminarflow to solve the NavierStokes and energy equations.The results indicate that inlet inclination angle has an impact on the indoor air quality(IAQ),which,in turn,affects the heat transfer distribution and thermal comfort within the cavity.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Air Quality Assessment of Ubeji Community near Petroleum-Related Activities

The escalating global concern over air pollution requires rigorous investigations. This study assesses air quality near residential areas affected by petroleum-related activities in Ubeji Community, utilizing Aeroqual handheld mobile multi-gas monitors and air quality multi-meters. Air sampling occurred on three distinct days using multi-gas monitors and meters, covering parameters such as CO, NO2, CH4, NH3, VOCs, Particulate Matter, Temperature, Relative Humidity, and Air Quality Index. Soil and plant samples were collected and analyzed for physicochemical and organic components. Air pollutant concentrations showed significant fluctuations. Carbon monoxide (CO) ranged from 0.00 to 3.22 ppm, NO2 from 0.00 to 0.10 ppm, CH4 from 4.00 to 2083 ppm, NH3 from 371 to 5086 ppm, and VOCs from 414 to 6135 ppm. Soil analysis revealed low total nitrogen, and undetected BTEX levels. Plant samples displayed a pH range of 7.72 to 9.45. CO concentrations, although below WHO limits, indicated potential vehicular and industrial influences. Fluctuations in NO2 and CH4 were linked to traffic, industrial activities, and gas flaring. NH3 levels suggested diverse pollution sources. The result in this study highlights the dynamic nature of air pollution in Ubeji community, emphasizing the urgent need for effective pollution control measures. Although CO concentrations were within limits, continuous monitoring is essential. Elevated NO2 levels gave information on the impact of industrial activities, while high CH4 concentrations may be associated with gas flaring and illegal refining. The study recommends comprehensive measures and collaborative efforts to address these complex issues, safeguarding both the environment and public health. This study shows the potential synergy between air quality sensors and plants for holistic environmental health assessments, offering valuable insights for environmental assessments and remediation endeavours. The findings call for stringent regulations and collaborative efforts to address air pollution in Ubeji community comprehensively.

Atmospheric Pollutants in a Commercial Region of Fortaleza, Ceará, Brazil: Integration of Health, Environment and Economy in Urban Planning to Improve Air Quality

The accelerated growth of the vehicular fleet, the modernization of large urban centers, and the few adjustments to the road network in Fortaleza have intensified the problems of traffic and emissions of atmospheric pollutants, highlighting the necessity for strategic urban planning initiatives to address the escalating issues of traffic and pollution. With the objective of analyzing the indices of concentrations of atmospheric pollutants and estimating how these levels can affect human health, this work consists of a study of the analysis of air quality in the intense trade region of Fortaleza. For this, the analysis zone was divided into three perimeters (Major - Medium - Minor), where each perimeter was analyzed at 7 am, 12 noon and 5 pm. Concentrations of the type of O3, particulate matter (PM2.5 and PM10), CO2 and HCHO were collected. Our results demonstrate that most of the analyses are within the limits of current legislation;however, at certain times and perimeters, the analyses of CO2 and HCHO exceeded the established limits. In view of the above, we conclude that public policies to control air quality are necessary to reduce the damage to human health and the environment caused by pollutants.

Exploring the Relationship between Spatiotemporal Variations in Air Quality and Meteorological Parameters before and during the COVID-19 Pandemic in Xi’an

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

Investigating the Air Quality Parameters in Louisiana’s Industrial Corridor: A Baton Rouge Case Study

In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.

Comparative Study of Evaluation Indicators for Learning Cities and the Implications

With the development of society,more and more cities are participating in the initiative to build learning cities.Constructing an evaluation indicator system for learning cities to monitor the progress and promote their growth has become increasingly important.This paper analyzes the preliminary framework of the UNESCO Global Learning City Index and R3L+Quality Framework.The comparison is made from the aspects of design philosophy,criteria of indicator,and the cycle of evaluation process.The findings suggest that the construction of an evaluation indicator system should be focused more on the diversity of learning city development,the construction of an evaluation process cycle,and the significance of building cooperative networks.

Comprehensive Review on Air Pollution Control Measures for Non-Attainment Cities of Uttar Pradesh, India

Introduction: The Indian state of Uttar Pradesh (UP) for the past many years has been reported to have many cities with highly polluted air quality. The state has been taking meticulous steps in combating air pollution in the form of action plans, introduced especially in its 17 non-attainment cities (NAC). To assess the progress and development of these action plans in UP, the present study has done an in-depth analysis and review of the state’s action plans and city micro action plans. Materials and Methods: In this research study, the analysis of the latest action plan reports, micro action plan reports as well as the recommendations for combating air pollution-related issues in the 17 NAC of the UP state has been well documented. Uttar Pradesh Pollution Control Board (UPPCB) has prepared these reports to highlight the progress of the plans in response to the growing air pollution in these cities. The information present in the reports has been used to further study sector-specific, category-specific action plans, institutional responsibility, and the present status of the action plans. Results: On average, the highest weightage in action plans was given to sector-specific categories such as Road dust and construction activities (24%). It was also observed that Urban local bodies (~50%) were majorly responsible to implement the action points and 56% of the action points were jointly implemented by multiple agencies.

Unveiling Citizens’ Perceptions, Attitudes, and Behaviors towards Air Quality: Insights from a Survey in the Po Basin Area

Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines the relationship between citizens’ knowledge, perceptions of air quality, attitudes towards policy measures, and intentions to adopt environmentally-friendly behaviors to combat air pollution. A comprehensive survey is conducted among a representative sample from seven regions in the Po basin area: Emilia-Romagna, Friuli-Venezia Giulia, Lombardy, Piedmont, Province of Trento, Valle d’Aosta, and Veneto. The survey aims at profiling participants based on their level of information, perceptions of air pollution, and attitudes towards emission-reducing behaviors. Cluster analysis identifies meaningful differences among citizen groups in terms of their awareness and intentions to engage in specific behaviors. Four distinct clusters emerge, each characterized by varying levels of willingness to embrace pro-environmental behaviors and support air quality improvement initiatives. By examining these profiles, the study uncovers patterns in citizens’ awareness, concerns, and acceptance of environmentally-friendly practices. The findings offer valuable insights for policymakers to develop targeted interventions, policies, and communication strategies.

Spatio-temporal Characteristics and Geographical Determinants of Air Quality in Cities at the Prefecture Level and Above in China

In recent years, the large scale and frequency of severe air pollution in China has become an important consideration in the construction of livable cities and the physical and mental health of urban residents. Based on the 2016-year urban air quality index(AQI) data published by the Ministry of Environmental Protection of China, this study analyzed the spatial and temporal characteristics of air quality and its influencing factors in 338 urban units nationwide. The analysis provides an effective scientific basis for formulating national air pollution control measures. Four key results are shown. 1) Generally, air quality in the 338 cities is poor, and the average annual values for urban AQI and air pollution in 2016 were 79.58% and 21.22%, respectively. 2) The air quality index presents seasonal changes, with winter > spring > autumn > summer and a u-shaped trend. 3) The spatial distribution of the urban air quality index shows clear north-south characteristic differences and a spatial agglomeration effect; the high value area of air pollution is mainly concentrated in the North China Plain and Xinjiang Uygur Autonomous Region. 4) An evaluation of the spatial econometric model shows that differences in urban air quality are due to social, economic, and natural factors.

Lockdown during COVID-19 pandemic:A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PMand NOconcentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis(DFA) during the lockdown period(March 24–April 20, 2020) and the corresponding period during the previous two years(i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PMand NOconcentration in urban cities, causing low concentrations. It is observed that the original PMand NOconcentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PMand NOconcentrations. Hurst exponent of the PMand NOconcentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations(APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.

Factor Analysis on Air Quality of Key Environmental Protection Cities

Statistical analysis was performed on 678 data of 6 key indices on air pollution in 113 key cities on basis of China Statistical Yearbook in 2015.Besides,factor analysis was then performed with SPSS17.0 to make overall assessment on air quality in different cities.The results showed that air pollution is quite severe in varying degrees in different cities and the research is expected to provide references for air pollution control in key environment protection cities.

How Do People Understand Urban Air Pollution? Exploring Citizens’ Perception on Air Quality, Its Causes and Impacts in Colombian Cities

Similarly to other Latin American countries, urban air quality is a major concern in Colombia. The purpose of the present study was to explore citizens’ perception on local air pollution, its causes and impacts. A questionnaire was individually applied to a sample of 994 participants. Relationships between demographic features and the perception on air quality were analyzed using a logistic regression, its odds ratio (OR), and a Chi-square test. Eighty percent of the respondents perceive their local air quality as either bad or fair, 65% recognize particulate matter as the main local air pollutant, and 90% recognize negative impacts on people’s health as the main consequence of air pollution. In contrast to other studies where age and economic status are variables highly related with public perception on the level of atmospheric pollution, in this study only the size of the cities has a statistically significant relationship with the perception on local air quality regarding the level of pollution(OR ≥ 1, confidence intervals > 1 and p-values < 0.05). While area sources such as mining and infrastructure construction are identified as the main sources of pollutant emissions in small towns (less than 50,000 inhabitants), in big cities, road traffic is identified as the main source contributor to atmospheric pollution. Opposite to data from official reports, and regardless of the size of the city, households are perceived as the place with greatest contribution to personal air pollution exposure. Results show that citizens are aware of the state of air quality and its health impact is a major concern. Such findings suggest people’s opinions can be used as provisional indicators in cities without data, as well as to monitor the results of local air quality management. As a complementary process, or at the same level of importance given to technical-based policy, citizen participation can contribute to a collective construction of urban air pollution control strategies.

Climatology of Air Quality in Arctic Cities—Inventory and Assessment

Freely available data of sulfur dioxide (SO2), ammonia (NH3), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM) observed in Arctic cities (north of 59.99 N) between 1972 and 2016 were compiled into an air-quality inventory of samples taken for limited periods. For cities with multiple years of data, air-quality climatology was determined in terms of daily means in the annual course. Mean urban air-quality climatology was calculated for regions of similar insolation, emission standards, topography, K&#246ppen-Geiger classification, and city size. Urban concentrations of PM precursors (SO2, NH3, NO2), PM2.5 and PM10 (PM with diameter less than 2.5 and 10 μm) were assessed in the sense of climatology with evidence from current knowledge. Typically, annual SO2 and NO2 means were lower for small than large Arctic cities, but can vary more than an order of magnitude over short distance. Cities seeing seasonal sea-ice had W-shaped mean annual courses of daily O3, while other cities had a spring maximum. Typically, annual means of urban pollutants in North America exceeded those in Scandinavia except for O3, where the opposite was true. Annual mean urban PM2.5 and PM10 concentrations varied from 1.6 to 21.2 μg·m-3 and 2 to 18.2 μg·m-3, respectively. Since PM10 encompasses PM2.5, annual PM10 means must be at least 21.2 μg·m-3. According to rural-to-urban ratios of species, seasonal transport of pollutants from wildfires, shipping, and the Kola Peninsula mining area occurred at some sites in Interior Alaska, western and northern Norway, respectively. Concurrent SO2 and PM or NO2 and PM measurements revealed combustion or traffic as major contributors to urban concentrations. Recommendations for potential future measurements of Arctic urban air quality were given based on the assessments of climatology and inventory.

Air Quality Regarding to TSP in Six Cities of Sonora,Mexico,a Criticism to the NOM-025-SSA1-1993 and a Proposed Criterion for Its Non-Compliance

In the present study, the air quality is assessed for the year 2010 regarding to the Total Suspended Particles (TSP) for six cities of Sonora, Mexico, representing the first regional study in Sonora in air quality. The assessment used performance indicators and indicators of compliance with the regulations. It is established that in all the cities the maximum limit value of daily concentration of 210 μg/m3 is exceeded, being the percentage of days above the rule of 30%, 78%, 76%, 6%, 3% and 62% for Agua Prieta, Nogales, Puerto Penasco, Hermosillo, Guaymas and Obregón respectively, classifying these days with poor air quality. According to the annualized index used, the air quality was not satisfactory for the period of study in the six cities. Nogales and Puerto Penasco presented the most adverse conditions of air quality with annual average values of TSP of 363 and 345 μg/m3 and maximum daily of 1047 and 1239 μg/m3 (498% and 590% above the norm) respectively. The requirements of coverage that establishes the Mexican Official Standard NOM-025-SSA1-1993 (SSA, 2005) are questioned for its compliance, proposing in this paper a criterion of non-compliance by prioritizing the protection of health and the precautionary principle. It is recommended to implement air quality management programs (PROAIRE) in these cities.