scholarly journals Relevance Analysis on the Variety Characteristics of PM2.5 Concentrations in Beijing, China

2018 ◽  
Vol 10 (9) ◽  
pp. 3228 ◽  
Author(s):  
Binxu Zhai ◽  
Jianguo Chen ◽  
Wenwen Yin ◽  
Zhongliang Huang
Keyword(s):  
Air Pollution ◽  
Spatial Distribution ◽  
Air Quality ◽  
Diurnal Variation ◽  
Wind Direction ◽  
Meteorological Factors ◽  
Distribution Characteristics ◽  
Wind Speeds ◽  
Pollutant Concentrations ◽  
Study Results

Air pollution has become one of the most serious environmental problems in the world. Considering Beijing and six surrounding cities as main research areas, this study takes the daily average pollutant concentrations and meteorological factors from 2 December 2013 to 13 October 2017 into account and studies the spatial and temporal distribution characteristics and the relevant relationship of particulate matter smaller than 2.5 μm (PM2.5) concentrations in Beijing. Based on correlation analysis and geo-statistics techniques, the inter-annual, seasonal, and diurnal variation trends and temporal spatial distribution characteristics of PM2.5 concentration in Beijing are studied. The study results demonstrate that the pollutant concentrations in Beijing exhibit obvious seasonal and cyclical fluctuation patterns. Air pollution is more serious in winter and spring and slightly better in summer and autumn, with the spatial distribution of pollutants fluctuating dramatically in different seasons. The pollution in southern Beijing areas is more serious and the air quality in northern areas is better in general. The diurnal variation of air quality shows a typical seasonal difference and the daily variation of PM2.5 concentrations present a “W” type of mode with twin peaks. Besides emission and accumulation of local pollutants, air quality is easily affected by the transport effect from the southwest. The PM2.5 and PM10 concentrations measured from the city of Langfang are taken as the most important factors of surrounding pollution factors to PM2.5 in Beijing. The concentrations of PM10 and carbon monoxide (CO) concentrations in Beijing are the most significant local influencing factors to PM2.5 in Beijing. Extreme wind speeds and maximal wind speeds are considered to be the most significant meteorological factors affecting the transport of pollutants across the region. When the wind direction is weak southwest wind, the probability of air pollution is greater and when the wind direction is north, the air quality is generally better.

scholarly journals Application of hysplit model to evaluate the relationship between meteorological parameters and PM10 content in the atmospheric environment in Ha Noi city, Vietnam

2020 ◽  
Vol 3 (2) ◽  
pp. First
Author(s):  
Anh Dung Nguyen ◽  
Hồng Sơn Dương ◽  
Đức Hạnh Nguyễn Thế ◽  
Nguyen Dac Dong
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Wind Direction ◽  
Meteorological Parameters ◽  
Meteorological Factors ◽  
Pollution Sources ◽  
Atmospheric Environment ◽  
Hysplit Model ◽  
The Relationship

Meteorology is one of the factors that plays an important role in assessing the quality of the atmospheric environment. Regarding the air pollution, especially dust and gaseous emissions, there are currently few studies on the relationship between meteorological factors and the increase in pollutant concentration. In this study, the relationship between several meteorological parameters such as temperature (TEM), wind speed (WS), wind direction (WD) and PM10 content in Hanoi were evaluated through the Spearman correlation coefficient (r) by SPSS statistical analysis software. Data includes hourly meteorological factors (temperature, wind speed and wind direction) and 24-h PM10 concentration collected at three automatic air quality monitoring stations in Hanoi in 2018. In addition, HYSPLIT model is used to determine the influence of wind direction and contribution of air pollution sources. The results show a negative correlation (r <0) between PM10 content, temperature and wind speed in dry and rainy seasons. During the dry season, Hanoi has a higher PM10 content than the remaining months of the year. This might be partly affected by outside pollution sources from the North and Northwest. The findings emphasize the dependence of air quality on local meteorological conditions and the distribution of major

scholarly journals Air Quality Assessment in the State of Kuwait during 2012 to 2017

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 678
Author(s):  
Adeeba Al-Hurban ◽  
Sawsan Khader ◽  
Ahmad Alsaber ◽  
Jiazhu Pan
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Meteorological Factors ◽  
Ambient Air ◽  
The State ◽  
Ambient Air Pollution ◽  
Northeast Monsoon ◽  
Residential Areas ◽  
Industrial Areas ◽  
The State Of Kuwait

This study aimed to examine the trend of ambient air pollution (i.e., ozone (O3), nitrogen monoxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), benzene (C6H6) and particulate matter with an aerodynamic diameter smaller than 10 microns (PM10), and non-methane hydrocarbons (NMHCs) at 10 monitoring stations located in the main residential and industrial areas in the State of Kuwait over 6 years (2012–2017). We found that the SO2 level in industrial areas (0.065 ppm) exceeded the allowable range of SO2 in residential areas (0.030 ppm). Air pollution variables were defined by the Environmental Public Authority of Kuwait (K-EPA). In this study, integrated statistical analysis was performed to compare an established air pollution database to Kuwait Ambient Air Quality Guidelines and to determine the association between pollutants and meteorological factors. All pollutants were positively correlated, with the exception of most pollutants and PM10 and O3. Meteorological factors, i.e., the ambient temperature, wind speed and humidity, were also significantly associated with the above pollutants. Spatial distribution mapping indicated that the PM10 level remained high during the southwest monsoon (the hot and dry season), while the CO level was high during the northeast monsoon (the wet season). The NO2 and O3 levels were high during the first intermonsoon season.

scholarly journals Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

2017 ◽  
Vol 17 (11) ◽  
pp. 7261-7276 ◽  
Author(s):  
Tobias Wolf-Grosse ◽  
Igor Esau ◽  
Joachim Reuder
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Large Eddy Simulation ◽  
Air Pollutants ◽  
Large Scale ◽  
Wind Speeds ◽  
Street Level ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Air Pockets

Abstract. Street-level urban air pollution is a challenging concern for modern urban societies. Pollution dispersion models assume that the concentrations decrease monotonically with raising wind speed. This convenient assumption breaks down when applied to flows with local recirculations such as those found in topographically complex coastal areas. This study looks at a practically important and sufficiently common case of air pollution in a coastal valley city. Here, the observed concentrations are determined by the interaction between large-scale topographically forced and local-scale breeze-like recirculations. Analysis of a long observational dataset in Bergen, Norway, revealed that the most extreme cases of recurring wintertime air pollution episodes were accompanied by increased large-scale wind speeds above the valley. Contrary to the theoretical assumption and intuitive expectations, the maximum NO2 concentrations were not found for the lowest 10 m ERA-Interim wind speeds but in situations with wind speeds of 3 m s−1. To explain this phenomenon, we investigated empirical relationships between the large-scale forcing and the local wind and air quality parameters. We conducted 16 large-eddy simulation (LES) experiments with the Parallelised Large-Eddy Simulation Model (PALM) for atmospheric and oceanic flows. The LES accounted for the realistic relief and coastal configuration as well as for the large-scale forcing and local surface condition heterogeneity in Bergen. They revealed that emerging local breeze-like circulations strongly enhance the urban ventilation and dispersion of the air pollutants in situations with weak large-scale winds. Slightly stronger large-scale winds, however, can counteract these local recirculations, leading to enhanced surface air stagnation. Furthermore, this study looks at the concrete impact of the relative configuration of warmer water bodies in the city and the major transport corridor. We found that a relatively small local water body acted as a barrier for the horizontal transport of air pollutants from the largest street in the valley and along the valley bottom, transporting them vertically instead and hence diluting them. We found that the stable stratification accumulates the street-level pollution from the transport corridor in shallow air pockets near the surface. The polluted air pockets are transported by the local recirculations to other less polluted areas with only slow dilution. This combination of relatively long distance and complex transport paths together with weak dispersion is not sufficiently resolved in classical air pollution models. The findings have important implications for the air quality predictions over urban areas. Any prediction not resolving these, or similar local dynamic features, might not be able to correctly simulate the dispersion of pollutants in cities.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

Experimental Study of Air Distribution Characteristics in Room of Stratum Ventilation under Two Kinds of Air Change Times

2013 ◽  
Vol 409-410 ◽  
pp. 668-672
Author(s):  
Yong Mei Xu ◽  
Jian Tang ◽  
Jun Han ◽  
Chu Qin Lin
Keyword(s):  
Experimental Study ◽  
Thermal Comfort ◽  
Experimental Studies ◽  
Air Distribution ◽  
Distribution Characteristics ◽  
Breathing Zone ◽  
Wind Speeds ◽  
Pollutant Concentrations ◽  
New Type ◽  
Better Than

Aimed at a new type of ventilation - stratum ventilation, air distributions at a breathing-zone in a model office were measured under kinds of air changes, the measure parameters in the experimental studies included temperatures, wind speeds and pollutant concentrations, based on which the thermal comfort at a breathing-zone were studied. Experimental results show that, the temperature, pollutant concentration and wind speeds in a breathing-zone under 5 times air changes are better than those under 6 times air changes. The calculating results of PMV and PPD indicate that the thermal comfort at a breathing-zone under 5 times air changes is better. The experimental study is instructive for the development of the ventilation.

From Google Maps to Air Quality: a big data approach to modelling real-time NO2 concentrations in an urban street canyon from road traffic data

2020 ◽  
Author(s):  
Helen Pearce ◽  
Zhaoya Gong ◽  
Xiaoming Cai ◽  
William Bloss
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Street Canyon ◽  
Box Model ◽  
Traffic Data ◽  
Flow Data ◽  
Pollutant Concentrations ◽  
Background Air ◽  
Model Chain

&lt;p&gt;In most European cities, the key air pollutants driving adverse health outcomes are nitrogen dioxide (NO2) and fine particulate matter (PM2.5), with 64% of new paediatric asthma cases in urban centres attributed to elevated NO2 levels (Achakulwisut et al., 2019). In the complex landscape of a city, a synthesis of techniques to quantify air pollution is required to account for variations in traffic, meteorology, and urban geometry.&lt;/p&gt;&lt;p&gt;Here, we present the results from a comparison study between measured air pollutant data collected at Marylebone Road, London and the output from a three-stage modelling chain. This site was chosen due to the availability of road-side air quality data collected within a street canyon (aspect ratio approximately equal to 1) and daily traffic flow in excess of 70,000 motor vehicles. The modelling chain consists of: 1) real-time traffic information of vehicle journey times, 2) speed-related emission calculations, and 3) air quality box-model to simulate the interaction of pollutants within the environment.&lt;/p&gt;&lt;p&gt;While the transport sector accounts for much of the outdoor air pollution in UK cities, a limiting factor of current techniques is that traffic is approximated at coarse temporal and spatial resolutions. In this study, we present a novel technique that helps to &amp;#8216;fill in&amp;#8217; the gaps in our traffic data by harnessing the power of real-time queries to Google Maps to obtain travel times between fixed locations, enabling the derivation of average vehicle speeds. This dataset can then be used to determine more accurate emission factors for NOx. Total emissions are then calculated with the aid of traffic flow data and vehicle fleet characteristics. The air quality box model simulates photochemical reactions that form NO2, the exchange of pollutants with the background air aloft, and advection of pollutants along the street.&lt;/p&gt;&lt;p&gt;Hourly travel times and total vehicle flow data were collected between July and October 2019, totalling 905 observations and calculated emissions values. Meteorological data from Heathrow airport and background air quality from the Kensington AURN site were used as supporting inputs to the air quality box model. Each observation was treated as a starting point of the box model, and the simulation was run for 1 hour, with mixing due to advection occurring every 60 seconds. Results are promising; when using the full model chain modelled and measured NO2 concentrations are significantly correlated (r = 0.467, p &lt; 0.000). In comparison, when a constant speed of 30 mph is used to calculate total emissions, therefore excluding the impact of congestion, the strength of the correlation decreases (r = 0.362, p &lt; 0.000) and the model underestimates pollutant concentrations.&lt;/p&gt;&lt;p&gt;The applications of this model chain are vast. For any street that is covered by a suitable mapping platform and has available data on vehicle numbers, it would be possible to provide a real-time estimation of pollutant concentrations at a high temporal resolution. This could be utilised in several ways, such as: assessing policy implementation, and providing a high resolution input for air quality modelling and health exposure studies.&lt;/p&gt;

Air Pollution and Seasonality Effects on Mode Choice in China

2017 ◽  
Vol 2634 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Weibo Li ◽  
Maria Kamargianni
Keyword(s):  
Air Pollution ◽  
Developing Countries ◽  
Air Quality ◽  
Positive Impact ◽  
Mode Choice ◽  
Urban Transport ◽  
Modal Shift ◽  
Pollution Levels ◽  
Study Results ◽  
The Impact

A modal shift from motorized to nonmotorized vehicles is imperative to reduce air pollution in developing countries. Nevertheless, whether better air quality will improve the willingness to use nonmotorized transport remains unclear. If such a reciprocal effect could be identified, a sort of virtuous circle could be created (i.e., better air quality could result in higher nonmotorized transport demand, which in turn could further reduce air pollution). Developing countries may, therefore, be more incentivized to work on air pollution reduction from other sources to exploit the extra gains in urban transport. This study investigated the impact of air pollution on mode choices and whether nonmotorized transport was preferred when air quality was better. Revealed preference data about the mode choice behavior of the same individuals was collected during two seasons (summer and winter) with different air pollution levels. Two discrete mode choice models were developed (one for each season) to quantify and compare the impacts of different air pollution levels on mode choices. Trip and socioeconomic characteristics also were included in the model to identify changes in their impacts across seasons. Taiyuan, a Chinese city that operates a successful bikesharing scheme, was selected for a case study. The study results showed that air quality improvement had a significant, positive impact on nonmotorized transport use, which suggested that improvements in air quality and promotion of nonmotorized transport must be undertaken simultaneously because of their interdependence. The results of the study could act as a harbinger to policy makers and encourage them to design measures and policies that lead to sustainable travel behavior.

scholarly journals Positive correlation between long term emission of several air pollutants and COVID-19 deaths in Sweden

2020 ◽  
Author(s):  
Lars Helander
Keyword(s):  
Air Quality ◽  
Population Density ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Threshold Value ◽  
Wind Speeds ◽  
Average Wind ◽  
Incidence And Mortality ◽  
Positive Correlations

AbstractSeveral recent studies have found troubling links between air pollution and both incidence and mortality of COVID-19, the pandemic disease caused by the virus SARS-CoV-2. Here, we investigate whether such a link can be found also in Sweden, a country with low population density and a relatively good air quality in general, with low background levels of important pollutants such as PM2.5 and NO2. The investigation is carried out by relating normalized emission levels of several air pollutants to normalized COVID-19 deaths at the municipality level, after applying a sieve function using an empirically determined threshold value to filter out noise. We find a fairly strong correlation for PM2.5, PM10 and SO2, and a moderate one for NOx. We find no correlation neither for CO, nor (as expected) for CO2. Our results are statistically significant and the calculations are simple and easily verifiable. Since the study considers only emission levels of air pollutants and not measurements of air quality, climatic and meteorological factors (such as average wind speeds) can trivially be ruled out as confounders. Finally, we also show that although there are small positive correlations between population density and COVID-19 deaths in the studied municipalities (which are for the most part rural and non densely populated) they are either weak or not statistically significant.

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