scholarly journals Synoptic meteorological modes of variability for fine particulate matter (PM<sub>2.5</sub>) air quality in major metropolitan regions of China

2018 ◽  
Vol 18 (9) ◽  
pp. 6733-6748 ◽  
Author(s):  
Danny M. Leung ◽  
Amos P. K. Tai ◽  
Loretta J. Mickley ◽  
Jonathan M. Moch ◽  
Aaron van Donkelaar ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Southern China ◽  
Central China ◽  
Meteorological Variables ◽  
Strong Correlations ◽  
Siberian High ◽  
Synoptic Weather ◽  
Bth Region

Abstract. In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small “climate benefit”, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

scholarly journals Synoptic meteorological modes of variability for fine particulate matter (PM<sub>2.5</sub>) air quality in major metropolitan regions of China

2017 ◽  
Author(s):  
Danny M. Leung ◽  
Amos P. K. Tai ◽  
Loretta J. Mickley ◽  
Jonathan M. Moch ◽  
Aaron van Donkelaar ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Negative Correlation ◽  
Southern China ◽  
Central China ◽  
Meteorological Variables ◽  
Climate Projections ◽  
Strong Correlations ◽  
Siberian High ◽  
Synoptic Weather ◽  
Bth Region

Abstract. In this study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ~ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis on 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 % to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian high, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (positive) and springtime fluctuation frequency of the Siberian high (negative). We apply the resulting PM2.5-to-climate sensitivities to IPCC Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by 2050s due to climate change, and find a modest decrease of ~ 0.1 μg m−3 in annual mean PM2.5 in the BTH region, which represents the compensating effects of enhanced relative humidity and synoptic frequency.

The impact of large-scale circulation on daily fine particulate matter (PM2.5) in major populated regions of China during winter 

2021 ◽  
Author(s):  
Zixuan Jia ◽  
Ruth Doherty ◽  
Carlos Ordóñez ◽  
Chaofan Li ◽  
Oliver Wild
Keyword(s):  
Particulate Matter ◽  
Relative Humidity ◽  
Large Scale ◽  
Southern China ◽  
East Asian ◽  
River Delta ◽  
Large Scale Circulation ◽  
East Asian Trough ◽  
Siberian High ◽  
Pollution Episodes

&lt;p&gt;With rapid economic growth and urbanization, air pollution episodes with high levels of particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) have become common in China. While emissions of pollutant precursors are important, meteorology also plays a major role in pollution episodes, especially in winter. We examine the influence of the dominant large-scale circulation and the key regional meteorological features on PM&lt;sub&gt;2.5&lt;/sub&gt; over three major regions of China: Beijing&amp;#8211;Tianjin&amp;#8211;Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The East Asian winter monsoon (EAWM) is primarily studied, including some of its main large-scale components such as the East Asian trough and the Siberian high, as it influences PM&lt;sub&gt;2.5 &lt;/sub&gt;differently in different parts of China. In the BTH region, the shallow East Asian trough curbs the invasion of northerly cold and dry air from the Siberian high which induces high relative humidity and heavy pollution, possibly via relative humidity-promoted aerosol formation and growth. A weak southerly wind in Eastern and Southern China associated with a weakened Siberian high suppresses horizontal dispersion, contributing to pollution accumulation over YRD. In addition, the El Ni&amp;#241;o-Southern Oscillation (ENSO) as the dominant mode of global ocean-atmosphere interaction has a substantial modulation on precipitation over southern China. In the PRD, weak southerly winds and precipitation deficits over southern China are conducive to atmospheric pollution possibly via reduced wet deposition. Furthermore, we construct new circulation-based indices based on the dominant large-scale circulation: a 500 hPa geopotential height-based index for BTH, a sea level pressure-based index for YRD and an 850 hPa meridional wind-based index for PRD. These three indices can effectively distinguish different levels of pollution over BTH, YRD and PRD, respectively. We also show how additional regional meteorological variables can improve the prediction of regional PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations for these three regions. These results are beneficial to understanding and forecasting the occurrence of severely polluted days for BTH, YRD and PRD from a large-scale perspective.&lt;/p&gt;

scholarly journals Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006–2018

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianyun Lu ◽  
Yanhui Liu ◽  
Xiaowei Ma ◽  
Meixia Li ◽  
Zhicong Yang
Keyword(s):  
High Risk ◽  
Relative Humidity ◽  
Southern Oscillation Index ◽  
Scrub Typhus ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Linear Relationships ◽  
Non Linear

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

scholarly journals Projected impacts of climate change on hydropower potential in China

2016 ◽  
Author(s):  
Xingcai Liu ◽  
Qiuhong Tang ◽  
Nathalie Voisin ◽  
Huijuan Cui
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
River Flow ◽  
Southern China ◽  
Eastern China ◽  
General Circulation Models ◽  
Central China ◽  
Climate Data ◽  
South Central ◽  
Hydropower Potential

Abstract. Hydropower is an important renewable energy source in China, but it is sensitive to climate change, because the changing climate may alter hydrological conditions (e.g., river flow and reservoir storage). Future changes and associated uncertainties in China's gross hydropower potential (GHP) and developed hydropower potential (DHP) are projected using simulations from eight global hydrological models (GHMs) forced by five general circulation models (GCMs) with climate data under two representative concentration pathways (RCP2.6 and RCP8.5). Results show that the estimation of the present GHP of China is comparable to other studies; overall, the annual GHP is projected to change by −1.7 to 2% in the near future (2020–2050) and increase by 3 to 6 % in the late 21st century (2070–2099). The annual DHP is projected to change by −2.2 to −5.4 % (0.7–1.7 % of the total installed hydropower capacity [IHC]) and −1.3 to −4% (0.4–1.3 % of total IHC) for 2020–2050 and 2070–2099, respectively. Regional variations emerge: GHP will increase in northern China, but decrease in southern China – mostly in South-Central China and Eastern China – where numerous reservoirs and large IHCs currently are located. The area with the highest GHP in Southwest China will have more GHP, while DHP will reduce in the regions with high IHC (e.g., Sichuan and Hubei) in the future. The largest decrease in DHP (in %) will occur in autumn or winter, when streamflow is relatively low and water use is competitive. Large ranges in hydropower estimates across GHMs and GCMs highlight the necessity of using multi-model assessments under climate change conditions. This study prompts the consideration of climate change in planning for hydropower development and operations in China.

scholarly journals Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1914 ◽  
Author(s):  
Liu ◽  
Liu ◽  
Chen ◽  
Labat ◽  
Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Reference Evapotranspiration ◽  
Southern Oscillation ◽  
Southern China ◽  
Sensitivity Coefficient ◽  
Meteorological Variables ◽  
The West ◽  
Average Temperature ◽  
Sunshine Hours ◽  
The Impact

This paper has adopted related meteorological data collected by 69 meteorological stations between 1951 and 2013 to analyze changes and drivers of reference evapotranspiration (ET0) in the hilly regions located in southern China. Results show that: (1) ET0 in southern China’s hilly regions reaches its maximum in summer and its minimum in winter, and that the annual ET0 shows an increasing trend. ET0 happened abrupt change due to the impact of abrupt meteorological variables changes, and the significant year of mutation were 1953, 1964 and 2008. Most abrupt changes of ET0 in meteorological stations occurred in the 1950s and 1960s. (2) The low value of ET0 was mainly captured in high-altitude areas. Spatially, the ET0 in the east was higher than that in the west. With the exception of a handful of stations, the trend coefficients of ET0 were all positive, exhibiting a gradual rise. Changes in ET0 in the east were much more sensitive than that in the west. Since ET0 was affected by the cyclical changes in relative humidity, short-period oscillations were observed in all these changes. (3) In general, the ET0 was negatively correlated with relative humidity, and positively correlated with temperature and sunshine percentage. ET0 is most sensitive to changes in average temperature, with a sensitivity coefficient of 1.136. ET0 showed positive sensitivity to average temperature and sunshine hours, which were notable in the northeastern, and uniform in the spatial. ET0 showed negatively sensitivity to relative humidity, and the absolute value of sensitivity coefficient in the northwestern is smaller. The highest contribution to ET0 is the average temperature (6.873%), and the total contribution of the four meteorological variables to the change of ET0 is 7.842%. The contribution of average temperature, relative humidity, and sunshine hours to ET0 is higher in the northern and eastern, northern, northern and eastern areas, respectively. Climate indexes (Western Pacific Index (WP), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNA), and El Niño-Southern Oscillation (ENSO)) were correlated with the ET0. In addition, the ET0 and altitude, as well as the latitude and longitude were also correlated with each other.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

scholarly journals Impact of Climate Change on Potential Distribution of Chinese White Pine Beetle Dendroctonus armandi in China

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 544
Author(s):  
Hang Ning ◽  
Ming Tang ◽  
Hui Chen
Keyword(s):  
Climate Change ◽  
Potential Distribution ◽  
Greenhouse Gas Emission ◽  
Central China ◽  
Qinling Mountains ◽  
Climate Scenarios ◽  
Impact Of Climate Change ◽  
Precipitation Seasonality ◽  
Suitable Area ◽  
The Impact

Dendroctonus armandi (Coleoptera: Curculionidae: Scolytidae) is a bark beetle native to China and is the most destructive forest pest in the Pinus armandii woodlands of central China. Due to ongoing climate warming, D. armandi outbreaks have become more frequent and severe. Here, we used Maxent to model its current and future potential distribution in China. Minimum temperature of the coldest month and precipitation seasonality are the two major factors constraining the current distribution of D. armandi. Currently, the suitable area of D. armandi falls within the Qinling Mountains and Daba Mountains. The total suitable area is 15.83 × 104 km2. Under future climate scenarios, the total suitable area is projected to increase slightly, while remaining within the Qinling Mountains and Daba Mountains. Among the climate scenarios, the distribution expanded the most under the maximum greenhouse gas emission scenario (representative concentration pathway (RCP) 8.5). Under all assumptions, the highly suitable area is expected to increase over time; the increase will occur in southern Shaanxi, northwest Hubei, and northeast Sichuan Provinces. By the 2050s, the highly suitable area is projected to increase by 0.82 × 104 km2. By the 2050s, the suitable climatic niche for D. armandi will increase along the Qinling Mountains and Daba Mountains, posing a major challenge for forest managers. Our findings provide information that can be used to monitor D. armandi populations, host health, and the impact of climate change, shedding light on the effectiveness of management responses.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

Sign in / Sign up

Export Citation Format

Share Document