scholarly journals The Temporal and Spatial Distribution of Hazy Days in Cities of Jiangsu Province China and an Analysis of Its Causes

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Jiansu Wei ◽  
Weijun Zhu ◽  
Duanyang Liu ◽  
Xiao Han
Keyword(s):  
Meteorological Data ◽  
Jiangsu Province ◽  
Industrial Emissions ◽  
Coal Consumption ◽  
Temporal And Spatial Distribution ◽  
Coastal Cities ◽  
Regional Temperature ◽  
Haze Formation ◽  
Increasing Trends ◽  
Haze Days

Based on the surface meteorological data of Jiangsu Province during 1980–2012, the climatic characteristics and the trends of haze were analyzed. The results indicated that during 1980–2012 haze days increased; in particular, severe and moderate haze days significantly increased. In the northern and coastal cities of Jiangsu Province China, haze days showed a significant increase. Haze often appeared in fall and winter and rarely in summer in the study area. It also occurred more often inland, and less along the coast. Haze occurred more often in June due to straw burning in the harvest time. The haze day increased during the 1990s over southern and southwestern Jiangsu Province; in central and northern Jiangsu, haze day increased after 2000. The continuous, regional, and regional continuous haze days all showed increasing trends. As the urban area expanded each year, industrial emissions, coal consumption, and car ownership increased accordingly, resulting in regional temperature increase and relative humidity decrease, which formed the urban heat island and dry island effects. Hence, haze formation and maintenance conditions became more favorable for more haze days, which led to the increase of haze days, and the significant increases of continuous, regional, and regional continuous haze days.

scholarly journals Innovation Institution and Spatial Transfer of Energy Industry: The Case of Jiangsu Province, China

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824401990018
Author(s):  
Xing Gao ◽  
Keyu Zhai ◽  
Yue Qiu ◽  
Mengqiu Cao ◽  
Meiling Wu
Keyword(s):  
Spatial Pattern ◽  
Fixed Effect Model ◽  
Jiangsu Province ◽  
Energy Industry ◽  
Industrial Upgrading ◽  
Patent Counts ◽  
Coastal Cities ◽  
Effect Model ◽  
D Investment ◽  
Local Research

This study aims to explore the effect of innovation institution on spatial transfer of energy industry in Jiangsu, China. We focus on the disparity of innovation and energy industry, and analyze the spatial transfer difference in different types of energy industry, rather than view energy industry as a whole. The study demonstrates the spatial change of energy industry at regional level and maps the spatial pattern at city level. The study chooses intellectual property rights (IPRs) protection intensity, authorization patents and local research and development (R&D) investment as the proxy of innovation. Using official data and employing panel fixed-effect model at city-industry level, we conclude (a) innovation abilities significantly influence the spatial transfer of energy industry in Jiangsu. Especially, due to the different time, IPRs protection, patent counts, and R&D investment have different effects on different regions in Jiangsu; (b) 2010 is an important turning point for energy industry development in Jiangsu, and after 2010, the energy industry begins to shift to the middle and northern Jiangsu, whereas the spatial pattern of energy industry in coastal cities is basically unchanged; (c) there is a great difference between the regions in Jiangsu Province, and industrial upgrading has not been achieved in northern Jiangsu.

Simplified Numerical Modeling of Energy Distribution in a Chinese Solar Greenhouse

2017 ◽  
Vol 33 (3) ◽  
pp. 291-304 ◽  
Author(s):  
Hui Xu ◽  
Yue Zhang ◽  
Tianlai Li ◽  
Rui Wang
Keyword(s):  
Energy Consumption ◽  
Northeast China ◽  
Field Experiments ◽  
Meteorological Data ◽  
Calculation Model ◽  
Energy Calculation ◽  
Coal Consumption ◽  
Energy Consumption Analysis ◽  
Solar Greenhouse ◽  
North Wall

Abstract.Solar greenhouses are widely used in northeast China to grow vegetables in winter. The energy consumption and distribution were determined in field experiments using a solar greenhouse in northeast China by monitoring the environmental factors inside and outside. Each surface inside greenhouse irradiated with incoming solar radiation was calculated. The greenhouse temperature including the front roof, north roof, north wall, canopy, and soil was calculated based on daily meteorological variables forecast by simulation modeling of each part and comparing with the results obtained using individual meteorological data under greenhouse conditions. Chinese greenhouse day and night energy consumption was calculated and compared. During the coldest days, the conduction energy reached 45% by day and 54% at night. The front roof accounted for the conduction energy loss (day: 54%, night: 68%). When the indoor temperature of the greenhouse was maintained above 15°C, the best time for greenhouse heating was around 5 a.m. and total coal consumption in three months was approximately 5.1 t. Results show that this numerical model simulated the various paths of greenhouse energy flow and heating processes. We estimated the specific daily coal consumption to define a comprehensive heating strategy. Keywords: Chinese greenhouse, Energy balance, Energy calculation model, Energy consumption analysis.

scholarly journals Analysis of Changes in Precipitation and Drought in Aksu River Basin, Northwest China

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yuhu Zhang ◽  
Wanyuan Cai ◽  
Qiuhua Chen ◽  
Yunjun Yao ◽  
Kaili Liu
Keyword(s):  
Meteorological Data ◽  
Standardized Precipitation Index ◽  
Northwest China ◽  
Precipitation Series ◽  
Significance Level ◽  
Spatiotemporal Trends ◽  
Increasing Trend ◽  
Almost All ◽  
Increasing Trends ◽  
Aksu River Basin

The analysis of the spatiotemporal trends of precipitation and drought is relevant for the future development and sustainable management of water resources in a given region. In this study, precipitation and Standardized Precipitation Index (SPI) trends were analyzed through applying linear regression, Mann–Kendall, and Spearman’s Rho tests at the 5% significance level. For this goal, meteorological data from 9 meteorological stations in and around Aksu Basin during the period 1960–2010 was used, and two main annual drought periods were detected (1978-1979 and 1983–1986), while the extremely dry years were recorded in 1975 and 1985 at almost all of the stations. The monthly analysis of precipitation series indicates that all stations had increasing trend in July, October, and December, while both increasing and decreasing trends were found in other months. For the seasonal scale, precipitation series had increasing trends in summer and winter. 33% of the stations had the decreasing trend on precipitation in the spring series, and it was 11% in the autumn. At the same time, the SPI-12 values of all stations had the increasing trend. The significant trends were detected at Aheqi, Baicheng, Keping, and Kuche stations.

scholarly journals Temperature and precipitation signal in two Alpine ice cores over the period 1961–2001

2012 ◽  
Vol 8 (6) ◽  
pp. 5867-5891 ◽  
Author(s):  
I. Mariani ◽  
A. Eichler ◽  
S. Brönnimann ◽  
R. Auchmann ◽  
T. M. Jenk ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Ice Core ◽  
Ice Cores ◽  
Snow Accumulation ◽  
Spatial Correlations ◽  
Annual Basis ◽  
Temperature And Precipitation ◽  
Time Period ◽  
Regional Temperature ◽  
Relationship Of

Abstract. Water stable isotope ratios and net snow accumulation in ice cores are usually interpreted as temperature and precipitation proxies. However, only in a few cases a direct calibration with instrumental data has been attempted. In this study we took advantage of the dense network of observations in the European Alpine region to rigorously test the relationship of the proxy data from two highly-resolved ice cores with local temperature and precipitation, respectively, on an annual basis. We focused on the time period 1961–2001 with the highest amount and quality of meteorological data and the minimal uncertainty in ice core dating (±1 yr). The two ice cores come from Fiescherhorn glacier (Northern Alps, 3900 m a.s.l.) and Grenzgletscher (Southern Alps, 4200 m a.s.l.). Due to the orographic barrier, the two flanks of the Alpine chain are affected by distinct patterns of precipitation. Therefore, the different location of the two ice cores offers the unique opportunity to test whether the precipitation proxy reflects this very local condition. We obtained a significant spatial correlation between annual δ18O and regional temperature at Fiescherhorn. Due to the pronounced intraseasonal to interannual variability of precipitation at Grenzgletscher, significant results were only found when weighting the temperature with precipitation. For this site, disentangling the temperature from the precipitation signal was thus not possible. Significant spatial correlations between net accumulation and precipitation were found for both sites but required the record from the Fiescherhorn glacier to be shifted by −1 yr (within the dating uncertainty). The study underlines that even for well-resolved ice core records, interpretation of proxies on an annual or even sub-annual basis remains critical. This is due to both, dating issues and the fact that the signal preservation intrinsically depends on precipitation.

scholarly journals Historical and Temporal Trends of Climatic Parameters in North East India

2017 ◽  
Vol 19 (4) ◽  
pp. 547-561 ◽  
Keyword(s):  
Meteorological Data ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Local Climate ◽  
Water Resources Systems ◽  
North East India ◽  
North East ◽  
Mk Test ◽  
Ne India ◽  
Increasing Trends

Accessing temporal trend of different meteorological parameters is essential for understanding the local climate changing pattern of a region. Quantitative estimates of the effect of climate change helps in understanding, planning, and management of water resources systems. In this study, monthly meteorological data were collected from 30 stations of north-east (NE) India for 1971–2010 and non-parametric Mann-Kendall (MK) test and Sen slope were employed for detection and quantification of significant temporal trends, respectively. An ESRI ArcGIS toolbar “ArcTrends” was used for the above mentioned tasks. The results obtained for rainfall were of mixed nature and both increasing and decreasing significant trends were found for different stations in different months. Most of the negative trends were found in the months of July–August (monsoon), whereas, more stations showed positive trends in April–May (pre-monsoon), and October–November (post-monsoon), indicating inter-seasonal shifting of rainfall without much change in the annual total. Number of rainy days was found to have positive trends in March–May (pre-monsoon) and negative trends in September–December. Except some positive trends during June–December in Manipur and Meghalaya, there were no significant trends in maximum temperature. In some stations, minimum temperature was found to have significant increasing trends throughout the year indicating a general rising trend in NE India. Some major towns like Guwahati, Imphal, Agartala and Kailashshahar showed significant positive trends in mean temperature, mostly during June–December. Mean relative humidity was, in general, found to be significantly increasing, especially during February–March. In some stations, wind speed was found to have significant negative trends throughout the year, with Agartala being the most affected.

Reducing urban new particle formation as a plausible solution to mitigate particulate air pollution in Beijing and other Chinese megacities

2020 ◽  
Author(s):  
Markku Kulmala ◽  
Lubna Dada ◽  
Federico Bianchi ◽  
Chao Yan ◽  
Keyword(s):  
Spatial Scales ◽  
Particle Formation ◽  
New Particle Formation ◽  
Subsequent Growth ◽  
Boundary Layer Height ◽  
Aerosol Mass ◽  
Interdisciplinary Approaches ◽  
Dimethyl Amine ◽  
Haze Formation ◽  
Haze Days

<p>With multi- and interdisciplinary approaches we show that atmospheric secondary particles are the dominating contributor to haze formation in terms of aerosol number, surface area and mass. Supported by our comprehensive observations in Beijing during 15 January 2018– 15 January 2020, we show that 80–90% of the aerosol mass (PM2.5) was formed via atmospheric reactions during the haze days and over 65% of the number concentration of haze particles resulted from urban new particle formation (NPF). Furthermore, the haze formation was much faster when the subsequent growth of newly formed particles was enhanced (rapid growth). We found that since the direct emissions of primary particles in Beijing has gone down significantly within recent years, all present-day haze episodes we preceded by a urban NPF event. We are also able to show that reducing the subsequent growth of freshly formed particles by a factor of 3-5 would delay the buildup of haze episodes by 1–3 days. Actually, this delay will decrease the length of each haze episode and the number of annual haze days could be approximately halved. The improvement can be achieved with targeted reduction of NPF precursors, mainly dimethyl amine, ammonia and further reductions of SO<sub>2</sub> emissions. Furthermore, reduction of anthropogenic VOC and nitrate emissions will slow down the growth rate of newly-formed particles and consequently reduce the haze formation. Our results show that the presence of haze decreases both boundary layer height and urban heat island intensity, which will further enhance haze particle number and mass concentrations over large spatial scales.</p><p> </p>

scholarly journals A teleconnection between sea surface temperature in the central and eastern Pacific and wintertime haze variations in southern China

2020 ◽  
Vol 143 (1-2) ◽  
pp. 349-359
Author(s):  
Xugeng Cheng ◽  
Jane Liu ◽  
Tianliang Zhao ◽  
Sunling Gong ◽  
Xiangde Xu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern China ◽  
Meteorological Conditions ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Pollutant Emissions ◽  
Haze Pollution ◽  
Haze Formation ◽  
Haze Days

AbstractHaze pollution in recent decades varies largely with both pollutant emissions and meteorological conditions. Using the discrete wavelet transform (DWT) method, we separate these two influences on haze variations in southern China in the time series of haze observations from 1981 to 2011. This helps us to identify the meteorological influence on interannual variation in haze occurrences in southern China and thus observe a teleconnection between the thermal forcing of sea surface temperature (SST) in the central and eastern Pacific and wintertime haze occurrences in southern China (R = − 0.51, p < 0.05). The total haze days in winter is highest among all seasons over southern China and the climotological mean of number of winter haze days is 7.5 days for the region. Compared with the normal winters, the regional mean of the number of haze days in southern China is reduced by ~ 5 days in the winters with above-normal Niño3.4 SST (during El Niño phases), but increased by ~ 4 days in the winters with below-normal Niño3.4 SST (during La Niña phases). In the warm SST winters, the cumulative consequences of strong winds, more precipitation, and a more unstable atmosphere with an “upper colder and lower warmer” vertical pattern leading to more ascendance can all hinder haze formation, whereas in the cold SST winters, opposite meteorological conditions are favorable to haze formation. These meteorological conditions induced by anomalous SST make wintertime haze pollution in southern China vary from year to year to a large extent. This study suggests a strong sensitivity of winter haze occurrences in southern China to the viability of the SST in the central and eastern Pacific.

scholarly journals Estimation of Surface and Near-Surface Air Temperatures in Arid Northwest China Using Landsat Satellite Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Yi Liu ◽  
Samuel Ortega-Farías ◽  
Fei Tian ◽  
Sufen Wang ◽  
Sien Li
Keyword(s):  
Spatial Distribution ◽  
Land Surface ◽  
Vegetation Index ◽  
Meteorological Data ◽  
Northwest China ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Temporal And Spatial Distribution ◽  
Near Surface ◽  
Temporal And Spatial

Near-surface air (Ta) and land surface (Ts) temperatures are essential parameters for research in the fields of agriculture, hydrology, and ecological changes, which require accurate datasets with different temporal and spatial resolutions. However, the sparse spatial distribution of meteorological stations in Northwest China may not effectively provide high-precision Ta data. And it is not clear whether it is necessary to improve the accuracy of Ts which has the most influence on Ta. In response to this situation, the main objective of this study is to estimate Ta for Northwest China using multiple linear regression models (MLR) and random forest (RF) algorithms, based on Landsat 8 images and auxiliary data collected from 2014 to 2019. Ts, NDVI (Normalized Difference Vegetation Index), surface albedo, elevation, wind speed, and Julian day were variables to be selected, then used to estimate the daily average Ta after analysis and adjustment. Also, the Radiative Transfer Equation (RTE) method for calculating Ts would be corrected by NDVI (RTE-NDVI). The results show that: 1) The accuracy of the surface temperature (Ts) was improved by using RTE-NDVI; 2) Both MLR and RF models are suitable for estimating Ta in areas with few meteorological stations; 3) Analyzing the temporal and spatial distribution of errors, it is found that the MLR model performs well in spring and summer, and is lower in autumn, and the accuracy is higher in plain areas away from mountains than in mountainous areas and nearby areas. This study shows that through appropriate selection and combination of variables, the accuracy of estimating the pixel-scale Ta from satellite remote sensing data can be improved in the area that has less meteorological data.

scholarly journals Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China

2020 ◽  
Author(s):  
Xinyu Fang ◽  
Wendong Liu ◽  
Jing Ai ◽  
He Mike ◽  
Ying Wu ◽  
...  
Keyword(s):  
Random Forest ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Moving Average ◽  
Arima Model ◽  
Jiangsu Province ◽  
Percentage Error ◽  
Practical Implementation ◽  
Infectious Diarrhea ◽  
Public Health Authorities

Abstract Background: Infectious diarrhea can lead to a considerable global disease burden. Thus, the accurate prediction of an infectious diarrhea epidemic is crucial for public health authorities. This study was aimed at developing an optimal random forest (RF) model, considering meteorological factors used to predict an incidence of infectious diarrhea in Jiangsu Province, China. Methods: An RF model was developed and compared with classical autoregressive integrated moving average (ARIMA)/X models. Morbidity and meteorological data from 2012 to 2016 were used to construct the models and the data from 2017 were used for testing. Results: The RF model considered atmospheric pressure, precipitation, relative humidity, and their lagged terms, as well as 1–4 week lag morbidity and time variable as the predictors. Meanwhile, a univariate model ARIMA(1,0,1)(1,0,0)52 (AIC=−575.92, BIC=−558.14) and a multivariable model ARIMAX(1,0,1)(1,0,0)52 with 0-1 week lag precipitation (AIC=−578.58, BIC=−578.13) were developed as benchmarks. The RF model outperformed the ARIMA/X models with a mean absolute percentage error (MAPE) of approximately 20%. The performance of the ARIMAX model was comparable to that of the ARIMA model with a MAPE reaching approximately 30%. Conclusions: The RF model fitted the dynamic nature of an infectious diarrhea epidemic well and delivered an ideal prediction accuracy. It comprehensively combined the synchronous and lagged effects of meteorological factors; it also integrated the autocorrelation and seasonality of the morbidity. The RF model can be used to predict the epidemic level and has a high potential for practical implementation.

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