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.

Urban Air Quality Plans and Integrated Assessment Methodologies

The air quality directive （2008/50/EC （of the European Parliament and of the Council of 21 May 2008）） requires European Member States to design appropriate AQPs （air quality plans） for zones and agglomerations where the air quality does not comply with the limit values and to assess possible emission reduction measures to decrease concentration levels. The Portuguese agglomeration of Porto Litoral is one of the several European Union urban areas that had to develop and implement AQPs to reduce particulate matter （PM10）. The AQPs were initially designed based on a scenario approach and using an air quality model, which was applied over the study region for the reference situation with the current PM10 emissions, and for a reduction scenario with PM10 emissions re-estimated considering the implementation of abatement measures. Aiming to cost-efficiently optimize Porto Litoral PM10 abatement measures, the assessment procedure was repeated using an optimization approach based on the RIAT ＋ （regional integrated assessment tool ＋）. Porto Litoral urban area＇s technical and non-technical measures were characterized （including associated costs） and, through the application of the air quality model to 20 emissions abatement scenarios, S-R （source-receptor） relationships were created. This paper comparatively describes the air quality plans designed to improve PM10 levels in the Porto Litoral agglomeration based on both the scenario analysis and the optimization approach.

Road transport and urban air quality in China

This paper takes a comprehensive view at transportationrelated air pollution problems and solution options. While the transport infrastructure in major Chinese cities is briefed, the environmental impacts of transport activities on urban air quality are discussed in detail. Not only the contribution of automotive emissions to the CO, NMHC and NO\-x pollution in urban areas is reviewed, but also concerns over the carcinogenic emissions, lead deposition and noise are presented. From a lifecycle view point, the impacts of motorway construction, as well as those from scrapped cars, car batteries and old tires in China have been taken into account. In the second part of this paper, the current technical efforts to curb vehicular emissions are evaluated. The performances of unleaded petrol, domestic catalytic converters, and fuel injection systems have been assessed. Options of cleaner alternative fuels and “green” vehicles are discussed, with both the life-cycle impacts and economic feasibility emphasized. Policy and management aspects for effective pollution control are tackled. In particular, speculations about potential economic incentives and legislative measures for vehicular pollution control are presented.

Urban Air Quality Assessment of Kathmandu by Passive Sampling Technique

This paper presents an assessment of air quality of the Kathmandu valley, capital city of Nepal. This is the largest urbanized area in the country of about 300 km2. In the last two decades an intensive development of demographic, traffic and industry growth in the region was observed which was reflected on the air quality degradation. In order to evaluate the urban air quality in Kathmandu, a field monitoring network for particle deposition, nitrogen dioxide （NO2） and sulphur dioxide （SO2） by passive sampling with high space resolution was implemented in co-operation with IVL-Sweden. The net work composed of 60 monitoring sites distributed in a grid of 1 × 1 km2 covering all over the main city and some rural valley. Monitoring were carried out for two seasons, rainy and dry as two campaign monitoring. The diffusive samplers were prepared and analyzed in IVL, Sweden. Good agreements in particle deposition between the two campaigns were observed. The particle deposition to the surrogate surface varied between 3 and 608 μg@cm2·monthl for the monitoring periods. The NOz concentrations on the other hand were quite similar in the two campaigns, and the SO2 concentrations were much lower in the first campaign compared to the second. The ranges of NO2 and SO2 concentrations were found to be from 5.6 to 52.6 μg/m3 and 0.6 to 23.4 μg·m-3 respectively. Arc Info/Arc map GIS 9.2 software was used for production of maps of spatial distribution of all the three parameters in the valley. Seasonal variation and traffic influence were also studied. Local meteorological effects in the distribution of pollutants were clearly observed and the NO2 concentrations were strictly related with traffic intensity.

Spatiotemporal evolution of urban air quality and socioeconomic driving forces in China

Air pollution is a serious problem brought by the rapid urbanization and economic development in China, imposing great challenges and threats to population health and the sustainability of the society. Based on the real-time air quality monitoring data obtained for each Chinese city from 2013 to 2014, the spatiotemporal characteristics of air pollution are analyzed using various exploratory spatial data analysis tools. With spatial econometric models, this paper further quantifies the influences of socioeconomic factors on air quality at both the national and regional scales. The results are as follows： （1） From 2013 to 2014, the percentage of days compliance of urban air quality increased but air pollution deteriorated and the worsening situation in regions with poor air quality became more obvious. （2） Changes of air quality show a clear temporal coupling with regional socioeconomic activities, basically ＂relatively poor at daytime and relatively good at night＂. （3） Urban air pollution shows a spatial pattern of ＂heavy in the east and light in the west, and heavy in the north and light in the south＂. （4） The overall extent and distribution of regional urban air pollution have clearly different characteristics. The formation and evolution of regional air pollution can be basically induced as ＂the pollution of key cities is aggravated--pollution of those cities spreads-- regional overall pollution is aggravated--the key cities lead in pollution governance--regional pollution joint prevention and control is implemented--regional overall pollution is reduced＂. （5） At the national level, energy consumption, industrialization and technological progress are the major factors in the worsening of urban air quality, economic development is a significant driver for the improvement of that quality. （6） Influenced by resources, environment and the development stage, the socioeconomic factors had strongly variable impacts on air quality, in both direction and intensity in different regions. Based on the conclusion, the regional differ- entiation and development idea of the relationship between economic development and en- vironmental changes in China are discussed.

Regional variation of urban air quality in China and its dominant factors

It is of great theoretical and practical importance to carry out research on the spatio-temporal evolution of urban air pollution and its driving forces,which helps to facilitate a deeper understanding of the mutual feedback mechanisms between the urban environment and socio-economic systems.Comprehension of these mechanisms will contribute to the design and implementation of efficient environmental policies that ultimately will improve the quality of urbanization development.This paper illustrates the spatio-temporal evolutionary characteristics of six urban ambient air pollutant concentrations,namely,CO,NO_(2),O_(3),PM_(10),PM_(2.5),SO_(2),in 286 sample cities above the prefecture level in China from 2014 to 2019.The interactions between the pollutant concentrations are analyzed based on panel regression models.A random forest model is then employed to explore the correlations between the concentrations of these six pollutants and 13 natural and socio-economic impact factors to isolate the most crucial ones.The results reveal three aspects.First,within the research period,the average annual concentration of O_(3)increased while that of other pollutants decreased year by year.Second,there were significant interactions between concentrations of the six pollutants,leading to obvious compound air pollution in urban areas.Third,the impact of natural and socio-economic factors on urban air quality varied greatly among different air pollutants,with air temperature,vegetation coverage,urbanization level and traffic factors ranking high and the different response thresholds to the dominant influencing factors.In light of the limited ability of humans to control the natural environment and meteorological conditions,it is recommended that urban air quality be further improved by optimizing urban density,controlling anthropogenic emission sources,and implementing strict air pollution prevention and control measures.

Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing:a review

Effectively monitoring urban air quality,and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions.Previous works,such as long-term obser-vations by monitoring stations,cannot provide customized data services and in-time emergency response under urgent situations(gas leakage incidents).Therefore,we first review the up-to-date approaches(often machine learning and optimization methods)with respect to urban air quality monitoring and hazardous gas source anal-ysis.To bridge the gap between present solutions and practical requirements,we design a conceptual framework,namely MAsmed(Multi-Agents for sensing,monitoring,estimating and determining),to provide fine-grained concentration maps,customized data services,and on-demand emergency management.In this framework,we leverage the hybrid design of wireless sensor networks(WSNs)and mobile crowdsensing(MCS)to sense urban air quality and relevant data(e.g.traffic data,meteorological data,etc.);Using the sensed data,we can create a fine-grained air quality map for the authorities and relevant stakeholders,and provide on-demand source term estimation and source searching methods to estimate,seek,and determine the sources,thereby aiding decision-makers in emergency response(e.g.for evacuation).In this paper,we also identify several potential opportunities for future research.

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.

Prediction of dust fall concentrations in urban atmospheric environment through support vector regression

Support vector regression （SVR） method is a novel type of learning machine algorithms, which is seldom applied to the development of urban atmospheric quality models under multiple socio-economic factors. This study presents four SVR models by selecting linear, radial basis, spline, and polynomial functions as kernels, respectively for the prediction of urban dust fall levels. The inputs of the models are identified as industrial coal consumption, population density, traffic flow coefficient, and shopping density coefficient. The training and testing results show that the SVR model with radial basis kernel performs better than the other three both in the training and testing processes. In addition, a number of scenario analyses reveal that the most suitable parameters （insensitive loss function e, the parameter to reduce the influence of error C, and discrete level or average distribution of parameters σ） are 0.001, 0.5, and 2 000, respectively.

Assessment of Urban Air Pollution and Spatial Spillover Effects in China: Cases of 113 Key Environmental Protection Cities

With rapid urbanization and energy consumption, environmental pollution and degradation have become increasingly serious problems in China. At the beginning of 2013, China implemented new ambient air quality standards（GB 3095-2012） in which the concentration of six pollutants including PM2.5, ozone, carbon monoxide, PM10, sulfur dioxide and nitrogen dioxide were monitored. This study gathered annual air pollutant concentration data for the six pollutants in 113 key environmental protection cites throughout China in 2014 and 2015 to explain spatial patterns of urban air pollution. Based on the Kernel density estimation method, spatial hotspots of air pollution were illustrated through which spatial cluster of each pollutants could be plotted. By employing an entropy evaluation system, urban air quality was assessed in terms of the six atmospheric pollutants. We conclude that, in general, CO and SO2 were two important pollutants in most Chinese cities, but this varied greatly among cities. The assessment results indicate that cities with the worst air quality were mainly located in northern and central provinces, dominantly in the Beijing-Tianjin-Hebei metropolitan area. Regression modeling showed that a combination of meteorological factors and human-related determinants, to say specifically, industrialization and urbanization factors, greatly influenced urban air quality variation in China. Results from spatial lag regression modeling confirmed that air pollution existed obvious spatial spillover effects among key cities. The spatial interdependence effects of urban air quality means that Chinese municipal governments should strengthen regional cooperation and deepen bilateral collaboration in terms of air regulation and pollution prevention.

Assessing the Impact of Using Different Land Cover Classification in Regional Modeling Studies for the Manaus Area,Brazil

Land cover classification is one of the main components of the modern weather research and forecasting models, which can influence the meteorological variable, and in turn the concentration of air pollutants. In this study the impact of using two traditional land use classifications, the United States Geological Survey (USGS) and the Moderate-resolution Imaging Spectroradiometer (MODIS), were evaluated. The Weather Research and Forecasting model (WRF, version 3.2.1) was run for the period 18 - 22 August, 2014 (dry season) at a grid spacing of 3 km centered on the city of Manaus. The comparison between simulated and ground-based observed data revealed significant differences in the meteorological fields, for instance, the temperature. Compared to USGS, MODIS classification showed better skill in representing observed temperature for urban areas of Manaus, while the two files showed similar results for nearby areas. The analysis of the files suggests that the better quality of the simulations favorable to the MODIS file is straightly related to its better representation of urban class of land use, which is observed to be not adequately represented by USGS.

Effect of traffic restriction on atmospheric particle concentrations and their size distributions in urban Lanzhou, Northwestern China

During the 2012 Lanzhou International Marathon, the local government made a significant effort to improve traffic conditions and air quality by implementing traffic restriction measures. To evaluate the direct effect of these measures on urban air quality, especially particle concentrations and their size distributions, atmospheric particle size distributions （0.5-20μm） obtained using an aerodynamic particle sizer （model 3321, TSI, USA） in June 2012 were analyzed. It was found that the particle number, surface area and volume concentrations for size range 0.5-10 μm were （15.0±2.1） cm-3, （11.8±2.6） μm2/cm3 and （1.9±0.6） μm2/cm3, respectively, on the traffic-restricted day （Sunday）, which is 63.2%, 53.0% and 47.2% lower than those on a normal Sunday. For number and surface area concentrations, the most affected size range was 0.5-0.7 and 0.5-0.8 μm, respectively, while for volume concentration, the most affected size ranges were 0.5-0.8, 1.7-2.0 and 5.0-5.4 μm. Number and volume concentrations of particles in size range 0.5-1.0μm correlated well with the number of non-CNG （Compressed Natural Gas） powered vehicles, while their correlation with the number of CNG-powered vehicles was very low, suggesting that reasonable urban traffic controls along with vehicle technology improvements could play an important role in improving urban air quality.