scholarly journals Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Jiaqi Tian ◽  
Chunsheng Fang ◽  
Jiaxin Qiu ◽  
Ju Wang
Keyword(s):  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Economic Cooperation ◽  
Ozone Pollution ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial ◽  
Pm2.5 And Pm10 ◽  
Peak Pattern ◽  
Pm2.5 Concentration

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

scholarly journals Assessment of ambient dust pollution status at selected point sources (residential and commercial) of Mingaladon area, Yangon region, Myanmar

2018 ◽  
Vol 32 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Sai Nyan Lin Tun ◽  
Than Htut Aung ◽  
Aye Sandar Mon ◽  
Pyay Hein Kyaw ◽  
Wattasit Siriwong ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Ambient Temperature ◽  
Traffic Congestion ◽  
Pm10 Concentration ◽  
Particulate Matters ◽  
Dust Pollution ◽  
Content Type ◽  
Pm2.5 And Pm10 ◽  
Pm2.5 Concentration

Purpose Dust (particulate matters) is very dangerous to our health as it is not visible with our naked eyes. Emissions of dust concentrations in the natural environment can occur mainly by road traffic, constructions and dust generating working environments. The purpose of this paper is to assess the ambient dust pollution status and to find out the association between PM concentrations and other determinant factors such as wind speed, ambient temperature, relative humidity and traffic congestion. Design/methodology/approach A cross-sectional study was conducted for two consecutive months (June and July, 2016) at a residential site (Defence Services Liver Hospital, Mingaladon) and a commercial site (Htouk-kyant Junction, Mingaladon) based on WHO Air Quality Reference Guideline Value (24-hour average). Hourly monitoring of PM2.5 and PM10 concentration and determinant factors such as traffic congestion, wind speed, ambient temperature and relative humidity for 24 hours a day was performed in both study sites. CW-HAT200 handheld particulate matters monitoring device was used to assess PM concentrations, temperature and humidity while traffic congestion was monitored by CCTV cameras. Findings The baseline PM2.5 and PM10 concentrations of Mingaladon area were (28.50±11.49)µg/m3 and (52.69±23.53)µg/m3, means 61.48 percent of PM2.5 concentration and 54.92 percent of PM10 concentration exceeded than the WHO reference value during the study period. PM concentration usually reached a peak during early morning (within 3:00 a.m.-5:00 a.m.) and at night (after 9:00 p.m.). PM2.5 concentration mainly depends on traffic congestion and temperature (adjusted R2=0.286), while PM10 concentration depends on traffic congestion and relative humidity (adjusted R2=0.292). Wind speed played a negative role in both PM2.5 and PM10 concentration with r=−0.228 and r=−0.266. Originality/value The air quality of the study area did not reach the satisfiable condition. The main cause of increased dust pollution in the whole study area was high traffic congestion (R2=0.63 and 0.60 for PM2.5 and PM10 concentration).

scholarly journals SEASONALITY DEDUCTION PLATFORM: FOR PM<sub>10</sub>, PM<sub>2.5</sub>, NO, NO<sub>2</sub> AND O<sub>3</sub> IN RELATIONSHIP WITH WIND SPEED AND HUMIDITY

2020 ◽  
Vol VI-4/W2-2020 ◽  
pp. 71-78
Author(s):  
S. Harbola ◽  
V. Coors
Keyword(s):  
Wind Speed ◽  
City Planning ◽  
Meteorological Data ◽  
Rank Correlation ◽  
Correlation Method ◽  
Time Frame ◽  
Temporal Variations ◽  
City Planner ◽  
Rank Correlation Method ◽  
Pm2.5 And Pm10

Abstract. Human and ecosystem health is affected by the risk of air pollution. A comprehensive understanding of the parameters generating pollution and governing their nature in time is essential to devise functional policies focusing on minimising the concentration of the pollutants. The effect of pollution parameters on meteorological data and existing in between relationships, have been the focus of the researcher’s planning of better city future. Thorough study of resources utilisation is required for contributing to framing effective, sustainable development, government policies management, and advance public services convenience. For protecting the environmental quality, renewable resources like solar and wind are more incorporated in techniques supporting better city planning. This paper considers the hourly time series Particular Matter (PM) PM2.5 and PM10, Nitrogen Oxide (NO), and Nitrogen Dioxide (NO2), and Ozone (O3) along with measured wind flow and humidity. This study’s objective is to assess the temporal seasonality patterns of these parameters in Stuttgart, Germany. The temporal variations over the city center in Stuttgart are analysed using unsupervised approach to perform seasonal hierarchical clustering on a series of parameters NO, NO2, O3, PM10, and PM2.5, wind speed and humidity. Furthermore, the correlations between meteorological and pollution parameters are analysed using the Spearman rank correlation method. Moreover, a dashboard is developed to provide the user desired time frame visualisation of these parameters. Proposed work would provide empirical meaning and seasonality comparison among the above mentioned parameters combined with interactive dashboard support. The analyses of the presented results clearly demonstrates the relationship between air pollutants, wind, humidity together in combine temporal activities frame. Thus, it would help city planner and policies maker with advanced knowledge of seasonality for meteorological and pollution parameters conditions.

scholarly journals Spatial and temporal variation of reference evapotranspiration under climate change: a case study in the Sanjiang Plain, Northeast China

2017 ◽  
Vol 49 (1) ◽  
pp. 251-265 ◽  
Author(s):  
Xinyi Song ◽  
Kui Zhu ◽  
Fan Lu ◽  
Weihua Xiao
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Negative Influence ◽  
Sanjiang Plain ◽  
Spatial And Temporal Variation ◽  
The Sanjiang Plain ◽  
Mountainous Regions

Abstract It is essential to understand the changing patterns in reference evapotranspiration (ET0) and its relation to climate variables. In this study, meteorological data obtained from the Sanjiang Plain (SJP) between 1959 and 2013 are used to calculate ET0 via the Penman–Monteith method. This study analyses the spatial and temporal changes of ET0 and determines which meteorological variables have an impact on this. The Mann–Kendall test, moving t-test, sensitivity analysis and simulated results have been used to conduct these analyses. The results demonstrate the following. (1) Spatially, there is an increasing trend in the annual ET0 values in agricultural areas. However, significant decreasing trends (P &lt; 0.05) can be found in mountainous regions. (2) Temporally, two abrupt changes can be detected in the early 1980s and the late 1990s for the entire SJP, leading to large inter-annual differences. (3) Sensitivity analysis shows that relative humidity (RH) is the most sensitive climate variable and has a negative influence on ET0, followed by temperature, sunshine duration and wind speed, all of which exert positive impacts. (4) The simulated result shows that ET0 is most sensitive to RH. However, significant reductions in wind speed can exert large influences on the ET0 values.

Variation Characteristics of PM10 Mass Concentrations and Correlations with Meteorological Factors in Xingtai City

2013 ◽  
Vol 807-809 ◽  
pp. 73-76
Author(s):  
Jian Wang ◽  
Mei Xu ◽  
Xia Ye ◽  
Wei Liu
Keyword(s):  
Wind Speed ◽  
Vapor Pressure ◽  
Negative Correlation ◽  
Mass Concentration ◽  
Meteorological Factors ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Air Pressure ◽  
Positive Correlation ◽  
Variation Characteristics

Based on monitoring data of air pollution index (API) and meteorological data from January 2009 to December 2012 in Xingtai, the variation characteristics of PM10mass concentration were analyzed and the relationships between PM10mass concentration and air pressure, wind speed, temperature, vapor pressure, relative humid and sunshine duration were investigated for four seasons using SPSS software. The results showed that the PM10mass concentration was 86.5, 83.3, 85.0 and 80.4 μg m­3, and the differences were not obvious tending to be a weak downward trend from 2009 to 2012. The percentage of the excellent and good air quality was high during the period and the mild and light pollution mainly appeared in December. The PM10mass concentration showed significant seasonal variations with a higher value in winter and fall and a lower value in spring and summer. The relationship between PM10mass concentration and meteorological factors showed some differences in different seasons. The PM10mass concentration had negative correlation with air pressure and sunshine duration and positive correlation with vapor pressure, temperature and wind speed in spring. The PM10mass concentration was negatively correlated with air pressure and sunshine duration, but positively correlated with vapor pressure and temperature in summer. The PM10mass concentration was negatively correlated with temperature, wind speed, vapor pressure and sunshine duration in fall. The PM10mass concentration had negative correlation with wind speed, sunshine duration and air pressure and positive correlation with vapor pressure and relative humidity in winter.

scholarly journals Nonlinear reconstruction of bioclimatic outdoor-environment dynamics for the Lower Silesia region (SW Poland)

2021 ◽  
Author(s):  
Arkadiusz Głogowski ◽  
Paolo Perona ◽  
Krystyna Bryś ◽  
Tadeusz Bryś
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Nonlinear System Identification ◽  
Climatic Conditions ◽  
Outdoor Environment ◽  
Reconstruction Method ◽  
Climatic Index ◽  
Bioclimatic Conditions

AbstractMeasured meteorological time series are frequently used to obtain information about climate dynamics. We use time series analysis and nonlinear system identification methods in order to assess outdoor-environment bioclimatic conditions starting from the analysis of long historical meteorological data records. We investigate and model the stochastic and deterministic properties of 117 years (1891–2007) of monthly measurements of air temperature, precipitation and sunshine duration by separating their slow and fast components of the dynamics. In particular, we reconstruct the trend behaviour at long terms by modelling its dynamics via a phase space dynamical systems approach. The long-term reconstruction method reveals that an underlying dynamical system would drive the trend behaviour of the meteorological variables and in turn of the calculated Universal Thermal Climatic Index (UTCI), as representative of bioclimatic conditions. At longer terms, the system would slowly be attracted to a limit cycle characterized by 50–60 years cycle fluctuations that is reminiscent of the Atlantic Multidecadal Oscillation (AMO). Because of lack of information about long historical wind speed data we performed a sensitivity analysis of the UTCI to three constant wind speed scenarios (i.e. 0.5, 1 and 5 m/s). This methodology may be transferred to model bioclimatic conditions of nearby regions lacking of measured data but experiencing similar climatic conditions.

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