Backward Trajectory and Multifractal Analysis of Air Pollution in Zhengzhou Region of China

With the continuous promotion of industrialization and urbanization, China's environmental pollution is becoming increasingly serious, which has caused considerable damage to the natural balance. Air pollution seriously harms people's physical and mental health, the ecological environment, and the social sustainable development of society. In this study, the backward trajectory model and multifractal methods were adopted to analyze air pollution in Zhengzhou. The backward trajectory analysis showed that most clusters of air pollution were from southern Hebei, eastern Shandong, and mid-western Henan, which were then transported to Zhengzhou. For the PSCF and CWT analyses, we selected four representative cities to explore how close the air pollution of Zhengzhou is to other areas on the basis of air polluted concentration. The results of several multifractal methods indicated that multifractality existed in the AQI time series of Zhengzhou and cross-correlations between Zhengzhou and each of the four cities. The widths of multifractal spectra showed that the air pollution in Zhengzhou was closest to that in Jinan, followed by Shijiazhuang, Zibo, and Luoyang. The CDFA analysis showed that carbon monoxide (CO), nitrogen dioxide (NO2), and inhalable particulate matter (PM10) had important influences on air pollution in Zhengzhou. These findings offer a useful reference for air pollution sources and their potential contributions in Zhengzhou, which can support policy makers in environmental governance and in achieving sustainable urban development.

Regional Air Pollutant Characteristics and Health Risk Assessment of Large Cities in Northeast China

This study systematically investigated the pollution characteristics of atmospheric O3 and PM2.5, regional transport, and their health risks in three provincial capitals in northeast China during 2016–2020. The results show that O3 concentrations showed a trend of high summer and low winter, while PM2.5 concentrations showed a trend of high winter and low summer during these five years. The results of the correlation analysis indicate that external sources contribute more O3, while PM2.5 is more from local sources. The backward trajectory clustering analysis results showed that Changchun had the highest share of northwest trajectory with a five-year average value of 67.89%, and the city with the highest percentage of southwest trajectory was Shenyang with a five-year average value of 23.95%. The backward trajectory clustering analysis results showed that the share of the northwest trajectory decreased and the share of the southwest trajectory increased for all three cities in 2020 compared to 2016. The results of the potential source contribution function (PSCF) and concentration weighting trajectory (CWT) analysis showed that the main potential source areas and high concentration contribution areas for PM2.5 in the northeast were concentrated in Mongolia, Inner Mongolia, Shandong Province, and the northeast, and for O3 were mainly located in Shandong, Anhui, and Jiangsu Provinces, and the Yellow Sea and Bohai Sea. The non-carcinogenic risk of PM2.5 in Harbin was high with a HQ of 2.04, while the other cities were at acceptable levels (HQ < 0.69) and the non-carcinogenic risk of O3 was acceptable in all three cities (HQ < 0.22). However, PM2.5 had a high carcinogenic risk (4 × 10−4 < CR < 0.44) and further treatment is needed to reduce the risk.

Characteristics and sources of atmospheric pollutants in typical inland cities in arid regions of central Asia: A case study of Urumqi city

The arid zone of central Asia secluded inland and has the typical features of the atmosphere. Human activities have had a significant impact on the air quality in this region. Urumqi is a key city in the core area of the Silk Road and an important economic center in Northwestern China. The urban environment is playing an increasingly important role in regional development. To study the characteristics and influencing factors of the main atmospheric pollutants in Urumqi, this study selected Urumqi’s daily air quality index (AQI) data and observation data of six major pollutants including fine particulate matter (PM2.5), breathable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3_8h) from 2014 to 2018 in conjunction with meteorological data to use a backward trajectory analysis method to study the main characteristics of atmospheric pollutants and their sources in Urumqi from 2014 to 2018. The results showed that: (1) From 2014 to 2018, the annual average of PM2.5, PM10, SO2, NO2 and CO concentrations showed a downward trend, and O3_8h concentrations first increased, then decreased, and then increased, reaching the highest value in 2018 (82.15 μg·m-3); The seasonal changes of PM2.5, PM10, SO2, NO2 and CO concentrations were characterized by low values in summer and fall seasons and high values in winter and spring seasons. The concentration of O3_8h, however, was in the opposite trend, showing the high values in summer and fall seasons, and low values in winter and spring seasons. From 2014 to 2018, with the exception of O3_8h, the concentration changes of the other five major air pollutants were high in December, January, and February, and low in May, June, and July; the daily changes showed a “U-shaped” change during the year. The high-value areas of the "U-shaped" mode formed around the 50th day and the 350th day. (2) The high-value area of AQI was from the end of fall (November) to the beginning of the following spring (March), and the low-value area was from April to October. It showed a U-shaped change trend during the year and the value was mainly distributed between 50 and 100. (3) The concentrations of major air pollutants in Urumqi were significantly negatively correlated with precipitation, temperature, and humidity (P<0.01), and had the highest correlation coefficients with temperature. (4) Based on the above analysis results, this study analyzed two severe pollution events from late November to early December. Analysis showed that the PM2.5/PM10 ratio in two events remained at about 0.1 when the pollution occurred, but was higher before and after the pollution (up to 1.46). It was shown that the pollution was a simple sandstorm process. Backward trajectory analysis clustered the airflow trajectories reaching Urumqi into 4 categories, and the trajectories from central Asia contributed the maximum values of average PM2.5 and PM10 concentrations.

Cold Fronts Transport Features of North China Pollutant over the Yangtze River Delta, China

&lt;p&gt;An air pollution process in Jiangsu Province, China on December 22&amp;#8211;23, 2016 is discussed by analyzing various data set, including the meteorological observation data, the reanalysis data from National Centers for Environmental Prediction (NCEP), the Air Quality Index (AQI), the PM&lt;sub&gt;2.5 &lt;/sub&gt;and PM&lt;sub&gt;10&lt;/sub&gt; concentrations data, and the airflow backward trajectory model of National Oceanic and Atmospheric Administration (NOAA). The results show that the air pollution episode was under the background of a medium cold front from the west of the Hetao area, and caused by regional transport of pollutants from North China. The primary pollutant was PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt;. The PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt; concentrations increase significantly 4&amp;#8211;6 h after the cold front passing and reached the peak in 13&amp;#8211;24 h. The obvious lag phenomena of the rising period and the peak-moment of PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt; concentrations were found at the Suzhou, Huai'an, Taizhou and Xuzhou stations, and the maximum of 3h-allobaric, the maximum and average values of the wind speed near the ground were larger one by one at the four stations respectively in the northwestern Jiangsu, north-central Jiangsu, along with the Yangtze river Jiangsu, and southeastern Jiangsu. The period of middle &amp;#8211;heave level pollution in Suzhou was 7&amp;#8211;9 h later than in Huai'an and Taizhou, and was 24 h later than in Xuzhou, because of the lower PM&lt;sub&gt;2.5&lt;/sub&gt; and PM&lt;sub&gt;10&lt;/sub&gt; concentrations at early December 21, the delay of pollutants from upstream, and the larger wind speed from the boundary layer to the surface in southeastern Jiangsu. WRF-Chem model can well reveal the pollutant transport process. The high-value zone has a close relationship with the position of cold front. At 1200 LST on December 22, the cold front reached Xuzhou accompanied by high PM&lt;sub&gt;2.5&lt;/sub&gt; concentration. At 1400 LST on December 22, the cold front advanced to Huai'an. The high PM&lt;sub&gt;2.5&lt;/sub&gt; concentration zone moved south alongside the cold front and covered Xuzhou and Huai'an. Suzhou, far away from the upstream, was less vulnerable to pollutant transport. The high-value did not fell until the northwest wind shifted to the north wind. The backward trajectory analysis of air pollution also indicated that regional transport of pollutants from North China led to the middle &amp;#8211;heave level pollution weather.&lt;/p&gt;

Impacts of meteorological conditions on PM2.5 and PM10 pollution in Zhengzhou, China

Meteorological conditions play an important role in aerosol pollution. In this study, the relationships between wind, temperature, relative humidity, and aerosol concentrations (PM2.5 and PM10) in Zhengzhou from January 2016 to December 2017 were analysed. Backward trajectory model was also used to investigate the relationship between meteorological parameters and regional transport of pollutants. Significant seasonal variations can be observed in the time series of pollutants and wind, temperature and relative humidity. The simulation of backward trajectories indicated that pollutants from southeast is critical to the air quality in Zhengzhou, in addition to local emissions of pollutants. To improve the air quality in Zhengzhou, joint efforts to reduce emissions in both Zhengzhou and its southeast adjacent regions should be considered.

On the structure of $ \alpha $-limit sets of backward trajectories for graph maps

<p style='text-indent:20px;'>In the paper we study what sets can be obtained as <inline-formula><tex-math id="M2">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-limit sets of backward trajectories in graph maps. We show that in the case of mixing maps, all those <inline-formula><tex-math id="M3">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-limit sets are <inline-formula><tex-math id="M4">\begin{document}$ \omega $\end{document}</tex-math></inline-formula>-limit sets and for all but finitely many points <inline-formula><tex-math id="M5">\begin{document}$ x $\end{document}</tex-math></inline-formula>, we can obtain every <inline-formula><tex-math id="M6">\begin{document}$ \omega $\end{document}</tex-math></inline-formula>-limits set as the <inline-formula><tex-math id="M7">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-limit set of a backward trajectory starting in <inline-formula><tex-math id="M8">\begin{document}$ x $\end{document}</tex-math></inline-formula>. For zero entropy maps, every <inline-formula><tex-math id="M9">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula>-limit set of a backward trajectory is a minimal set. In the case of maps with positive entropy, we obtain a partial characterization which is very close to complete picture of the possible situations.</p>