Search

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades. [ABSTRACT FROM AUTHOR]

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970 s. In most studies, the weekend effect is referred to the change of O 3 , i.e., lower emission of NO x on weekends leads to a higher concentration of O 3 . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O 3 on the peak and the valley of the emission scenario estimated by the NO 2 concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O 3 corresponding to the weekly cycle emission of NO x , i.e., O 3 of the peak time of NO 2 is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels. [ABSTRACT FROM AUTHOR]

With the development of industry, air pollution has become a serious problem. It is very important to create an air quality prediction model with high accuracy and good performance. Therefore, a new method of CT-LSTM is proposed in this paper, in which the prediction model is established by combining chi-square test (CT) and long short-term memory (LSTM) network model. CT is used to determine the influencing factors of air quality. The hourly air quality data and meteorological data from Jan. 1, 2017 to Dec. 31, 2018 are used to train the LSTM network model. The data from Jan. 1, 2019 to Dec. 31, 2019 are used to evaluate the LSTM network model. The AQI level of Shijiazhuang of Hebei Province of China from Jan. 1, 2019 to Dec. 31, 2019 is predicted with five methods (SVR, MLP, BP neural network, Simple RNN and this paper's new method). Then, a contrastive analysis of the five prediction results is made. The experimental results show that the accuracy of this new method reaches 93.7%, which is the highest in the five methods and the maximum error of this new method is 1. The correct number of days predicted by this new method is also the highest among the five methods, which is 342 days. The new method also shows good characteristics in MAE, MSE and RMSE, which makes it more accurate for people to predict the AQI level. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic relationship between ife expectancy (LE) and its inflencing factors including, health care, socioeconomic, and environment factors in Beijing City of China. Time-series data from 2000 to 2018 were collected and vector autoregression (VAR) modeling was performed. This study quantified the lagged effect of air pollution and socioeconomic factors on increased LE over the 19 years of the study period in Beijing. The results showed that a VAR model with optimum lag 3 was constructed between LE and three explanatory variables including per capita area of green land (AGL), the average wage of employed staff and workers (WAGE), and PM2.5. In addition, LE showed a decrease from its present value once the impulse of AGL and WAGE are given. In contrast, LE increase from its present value after the impulse of PM2.5 given in the ten-year period analysis. In sum, environment factors such as air pollution and area of green land are considered to be highly effective in influencing LE than socioeconomic factors in Beijing City. [ABSTRACT FROM AUTHOR]

The development of industry has brought about the pollution of the atmospheric environment. Pollution is harmful to people's health. Realizing the real-time monitoring of atmospheric environmental quality parameters can improve the above-mentioned effects. China's existing environmental monitoring systems focus on the accuracy of the system hardware itself for assessment, lack of data analysis and forecasting and early warning, and cannot provide managers and ordinary people with decision-making and activity guidance. This paper develops an IPV6-based high-spatial–temporal precision air pollutant monitoring and early warning platform. The feasibility of the system is verified through networking tests, operation tests, and early warning tests. Through actual data analysis and comparison, it is concluded that the monitoring system has field feasibility, and the atmospheric environment monitoring for the target observation area has achieved the desired observation function. This system integrates GIS technology and B/S architecture to analyze changes in the regional environment to provide support for regional environmental air quality management. The forecast and early warning module constructed by combining the weight method of the influence of different input factors on the environmental quality index and minute-level observations can provide technical support for the government to improve the level of supervision. [ABSTRACT FROM AUTHOR]

Air pollution has been an environmental problem exerting serious impact on human health. An accurate prediction of air pollutant concentrations in space and time is essential to the mitigation and minimization of exposure to air pollution, particularly in cities. To take advantage of data collected at monitoring stations and the effect of secondary information such as meteorological factors, this paper proposes a flexible hierarchical Bayesian model (HBM) which can predict air pollutant concentrations in space and time. Within the framework, spatial temporal Kriging (STK) is employed for interpolation and the obtained results are used as priors. Because of the undesirable performance of the STK, the likelihood in the form of a nonlinear regression (NLR) with skewed normal residuals is employed to take into account the effect of meteorological factors to derive the posterior distribution of air pollutant concentrations. Due to high dimensionality and complexity, the formulation of the posterior distribution is non-analytic. We thus need to draw samples from the estimated parameters of the posterior distribution with Markov chain Monte Carlo (MCMC) method, and approximate the population characteristics with the sample characteristics. We evaluate the HBM with the concentrations of air pollutants and the meteorological variables from Northern China. For all pollutants, in the cross-validation (CV) experiments, the HBM reduces the root mean square error (RMSE) of NLR and STK by at least 0.08 and 0.4, and R 2 by at least 0.02 and 0.4. The empirical results show that the proposed HBM generally outperforms the NLR and STK in terms of efficiency, accuracy and robustness. The proposed framework is general enough for the analysis of spatial-temporal data of all kinds. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most serious threats to human health and is an issue causing growing public concern. Air quality forecasts play a fundamental role in providing decision-making support for environmental governance and emergency management, and there is an imperative need for more accurate forecasts. In this paper, we propose a novel spatial–temporal deep multitask learning (ST-DMTL) framework for air quality forecasting based on dynamic spatial panels of multiple data sources. Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records that were generated on an hourly basis from January 2013 to September 2017 in China. An application check is also conducted to verify the practical value of our proposed ST-DMTL framework. Our empirical results indicate the efficacy of the framework as a viable approach for air quality forecasts. [ABSTRACT FROM AUTHOR]

As a kind of particulate matter which is very detrimental to human health, the prediction of PM 2.5 has attracted increasing attention in air pollution forecasting and early warning. In this paper, density peaks clustering (DPC) is employed to select the individual models for combination forecasting, and a novel high-precision combination forecasting model named DPC-based combined model is proposed to predict PM 2.5 concentrations. Firstly, CEEMD is introduced to decompose the original P M 2.5 data into three intrinsic mode functions ( I M F s ) with different features. Then, 125 individual models are obtained by applying SVR and GRNN to these three I M F s , and the intelligent optimization algorithms are adopted to optimize the parameters of SVR. Finally, the 125 individual models are clustered by DPC based on five model evaluation indexes to select the individual model for combination forecasting. The experimental results of four Chinese cities demonstrate that the proposed model outperforms the optimal individual model and can greatly improve the prediction accuracy. Taking Dalian as an example, the MAE of the proposed model is 2.2 by clustering the 125 individual models into two groups, and the result reveals that the accuracy is increased by 65.73% compared with the minimum MAE (6.42) of the individual models. [ABSTRACT FROM AUTHOR]

With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people's health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]

Using ArcGIS to analyze satellite derived PM2.5 estimates, this paper obtains the average concentration and maximum concentration of fine particulate matter (PM2.5) in China's 31 provinces from 2002 to 2015. We adopt fixed effects model and spatial Durbin model to investigate the association between PM2.5 and perinatal mortality rates. The results indicate that PM2.5 has a significantly positive association with perinatal mortality rates. A 1% increase of log-transformed average concentration and maximum concentrations of PM2.5 is associated with 1.76‰ and 2.31‰ increase of perinatal mortality rates, respectively. In spatial econometrics analysis, we find PM2.5 has significant spatial autocorrelation characteristics. The concentrations of log-transformed average and maximum PM2.5 increase 1% is associated with a 2.49% increase in a 2.49‰ and 2.19‰ increase of perinatal mortality rates, respectively. The potential mechanism is that air pollution has an impact on infant weight to impact perinatal mortality rates. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved. [ABSTRACT FROM AUTHOR]

The current nationally determined contributions, pledged by the countries under the Paris Agreement, are far from limiting climate change to below 2 ∘C temperature increase by the end of the century. The necessary ratcheting up of climate policy is projected to come with a wide array of additional benefits, in particular a reduction of today's 4.5 million annual premature deaths due to poor air quality. This paper therefore addresses the question how climate policy and air pollution–related health impacts interplay until 2050 by developing a comprehensive global modeling framework along the cause and effect chain of air pollution–induced social costs. We find that ratcheting up climate policy to a 2 ∘ compliant pathway results in welfare benefits through reduced air pollution that are larger than mitigation costs, even with avoided climate change damages neglected. The regional analysis demonstrates that the 2 ∘C pathway is therefore, from a social cost perspective, a "no-regret option" in the global aggregate, but in particular for China and India due to high air quality benefits, and also for developed regions due to net negative mitigation costs. Energy and resource exporting regions, on the other hand, face higher mitigation cost than benefits. Our analysis further shows that the result of higher health benefits than mitigation costs is robust across various air pollution control scenarios. However, although climate mitigation results in substantial air pollution emission reductions overall, we find significant remaining emissions in the transport and industry sectors even in a 2 ∘C world. We therefore call for further research in how to optimally exploit climate policy and air pollution control, deriving climate change mitigation pathways that maximize co-benefits. [ABSTRACT FROM AUTHOR]

Urban air pollution is the most serious environmental pollution problem in China. It not only causes serious losses to our economy, but also brings great hidden dangers to the physical and mental health of urban residents. Therefore, it is urgent to prevent and control air pollution. Air quality prediction and forecasting must be carried out to prevent air pollution. Timely and accurate air quality prediction can not only help urban managers to make scientific and effective preventive measures, but also provide more healthy and safe travel strategies for urban residents. China's air monitoring system is gradually improving, its scale is expanding, and a large amount of air quality data is accumulating. With the rapid expansion of data scale, the traditional method of air quality prediction technology has been unable to deal with these massive data. In this paper, 365 sets of air pollutant data from January 1, 2018 to December 31, 2018 in Shen Zhen were used as experimental objects. The improved SAPSO algorithm and PSO algorithm were used to optimize the parameters of SVM model and construct the air quality evaluation model. By analyzing the classification results of air quality grade, selecting relevant data and using partial least squares, the correlation coefficient matrix is established for the classification results, and the pollutant factors affecting air quality in Shenzhen are obtained. The results are ideal, which provides a scientific theoretical basis for the prevention and control of air pollution and urban management planning. [ABSTRACT FROM AUTHOR]

China's polluted air has seriously affected the health of the Chinese population. This paper uses attribution theory to investigate how Chinese newspapers and non-Chinese newspapers presented smog in China and attributed causes and responsibilities. Built on previous studies, this study adopted covariation model within attribution theory and painted an interesting picture of smog coverage by Chinese and non-Chinese newspapers because of the apparent differences in perspectives. The criteria further provided folder for elaboration on explaining why the different coverages attributed causes as they did. Implications are discussed. [ABSTRACT FROM AUTHOR]

In January 2012, Beijing began releasing air quality report of PM 2.5. The move has been credited as a major breakthrough in China’s environmental governance as it is argued that online environmental activists in China were the major driving force behind it. Since then, the issue of air pollution has reemerged as one of the hottest issues and a series of new policies addressing air pollution have been adopted. Facing a relatively new phenomenon (wumai) and heightened public concerns, to what extent is the state still capable of guiding the pubic perception and opinions about the issue? By analyzing the discourse of air pollution in People’s Daily and comparing it with public opinion survey data, the paper finds that the state’s ability to guide public opinion is rather limited, indicating that opportunities do exist for civil society to play a bigger role in China’s environmental governance. [ABSTRACT FROM AUTHOR]

Air deterioration caused by pollution has harmed public health. The existing studies on the economic loss caused by a variety of air pollutants in multiple cities are lacking. To understand the effect of different pollutants on public health and to provide the basis of the environmental governance for governments, based on the dose-response relation and the willingness to pay, this paper used the latest available data of the inhalable particulate matter (PM) and sulphur dioxide (SO) from January 2015 to June 2015 in 74 cities by establishing the lowest and the highest limit scenarios. The results show that (1) in the lowest and highest limit scenario, the health-related economic loss caused by PM and SO represented 1.63 and 2.32 % of the GDP, respectively; (2) For a single city, in the lowest and the highest limit scenarios, the highest economic loss of the public health effect caused by PM and SO was observed in Chongqing; the highest economic loss of the public health effect per capita occurred in Hebei Baoding. The highest proportion of the health-related economic loss accounting for GDP was found in Hebei Xingtai. The main reason is that the terrain conditions are not conducive to the spread of air pollutants in Chongqing, Baoding and Xingtai, and the three cities are typical heavy industrial cities that are based on coal resources. Therefore, this paper proposes to improve the energy structure, use the advanced production process, reasonably control the urban population growth, and adopt the emissions trading system in order to reduce the economic loss caused by the effects of air pollution on public health. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of air pollution on people's interest in emigration. Using an online search index on 'emigration' which is positively correlated with its search volume, we develop a city-by-day measurement of people's emigration sentiment. We find that searches on 'emigration' will grow by approximately 2.3-4.8% the next day if today's air quality index (AQI) is increased by 100 points. In addition, such an effect is more pronounced when the AQI level is above 200, a sign of 'heavily polluted' and 'severely polluted' days. We also find that such effect differs by destination countries and by metropolitan areas. [ABSTRACT FROM AUTHOR]

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities. [ABSTRACT FROM AUTHOR]

Due to rapid economic growth of the country in the last 25 years, particulate matter (PM) has become a topic of great interest in China. The rapid development of industry has led to an increase in the haze created by pollution, as well as by high levels of urbanization. In 2012, the Chinese National Ambient Air Quality Standard (NAAQS) imposed 'more strict' regulation on the PM concentrations, i.e., 35 and 70 μg/m for annual PM and PM in average, respectively (Grade-II, GB3095-2012). The Pearson's correlation coefficient was used to determine the linear relationship of pollution between pollution levels and weather conditions as well as the temporal and spatial variability among neighbouring cities. The goal of this paper was to investigate hourly mass concentration of PM and PM from June 1 to August 31, 2015 collected in the 11 largest cities of Gansu Province. This study has shown that the overall average concentrations of PM and PM in the study area were 26 and 66 μg/m. In PM episode days (when concentration was more than 75 μg/m for 24 hrs), the average concentrations of PM was 2-3 times higher as compared to non-episode days. There were no observed clear differences during the weekday/weekend PM and other air pollutants (SO, NO, CO and O) in all the investigated cities. [ABSTRACT FROM AUTHOR]

China, as a fast growing fossil-fuel-based economy, experiences increasing levels of air pollution. To tackle air pollution, China has taken the first steps by setting emission-reduction targets for nitrogen oxides (NO) and sulphur dioxide (SO) in the 11th and 12th Five Year Plans. This paper uses two models-the Energy-Environment-Economy Model at the Global level (E3MG) and the global Chemistry Transport Model pTOMCAT-to test the effects of these policies. If the policy targets are met, then the maximum values of 32 % and 45 % reductions below 'business as usual' in the monthly mean NO and SO concentrations, respectively, will be achieved in 2015. However, a decrease in NO concentrations in some highly polluted areas of East, North-East and South-East China can lead to up to a 10% increase in the monthly mean concentrations in surface ozone in 2015. Our study demonstrates an urgent need for the more detailed analysis of the impacts and designs of air pollution reduction guidelines for China. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative method to decompose carbon emissions changes using distance functions estimated by data envelopment analysis. The proposed approach can measure the effects of technical efficiency change and technical change, the effects of substitution among inputs, and the effects of GDP composition change on the changes in carbon emissions. We apply this method to decompose the carbon emissions at China's provincial levels. For the 30 provinces as a whole during the 11th Five-Year Plan period (2006-2010), empirical results demonstrated that economic growth and the potential carbon factor changes were the largest contributors to carbon emissions increase, while the good output technical changes and energy usage technical changes were the largest components to carbon emissions reduction. The structure effects represented by GDP composition change was a mild contributor to carbon emissions reduction for most provinces. We found that the substitution effects among inputs existed and contributed to carbon emissions increase for most provinces, but their impacts were limited and differed across provinces. The impacts of the good output technical efficiency change and energy usage technical efficiency changes were extremely limited and different across provinces. The empirical results are beneficial to China's central and local governments to make regional adaptive policies to carbon emissions reduction. [ABSTRACT FROM AUTHOR]

This paper studies the interplay between climate, health, and the economy in a stylized world with eleven heterogeneous regions, with special emphasis on USA, Europe, China, India, and Africa. We introduce health impacts into a simple economic integrated assessment model where both the local cooling effect of S O and the global warming effect of C O are endogenous, and investigate how these factors affect the equilibrium path. Regions do not respond in the same way to climate change. In particular, emission abatement rates and health costs depend on the economic and geographical characteristics of each region. Two policy scenarios are considered, Nash and Optimal, for which we present both global and regional results. Results for Africa and China are highlighted. [ABSTRACT FROM AUTHOR]

This paper uses choice modeling surveys from the Chinese cities of Jiujiang, Jiangxi Province, and Changsha, Hunan Province, to identify residential preferences for simultaneously increasing the stability of the electricity supply and decreasing the health risks from air pollution. Air pollution in China is mainly attributable to externalities associated with the electricity supply. We employ a contingent ranking approach as our choice modeling method and test for the transfer of benefits for these preferences between the two sites. The original benefit estimates indicate that the implicit price for reducing the number of power breakdowns is about RMB 83 per times × year × household in Jiujiang and RMB 78 per times × year × household in Changsha, while the implicit price for reducing the duration of power breakdowns is statistically zero in Jiujiang and RMB 71 per times × year × household in Changsha. From the alternative perspective, we estimate that the annualized value of the statistical lifetime risk of cancer caused by air pollution over a 70-year period is RMB 50,844 per year in Jiujiang and RMB 67,146 per year in Changsha. This suggests that we do not reject benefit transferability based on the implicit price of the number of power breakdowns, but do reject it based on the number of deaths from cancer caused by air pollution. [ABSTRACT FROM AUTHOR]

This paper reports an investigation on the periodic variation of pollutant levels at a typical traffic intersection of Hong Kong. Carbon monoxide, carbon dioxide and particulate matters (PM) were measured respectively and the measured data show periodic variations with the traffic signal intervals. The power spectral density (PSD) approach was used to inspect the trends and periodic oscillations of measured pollutants. Singular spectrum analysis was applied to decompose the measured data into statistically significant non-linear trends and oscillations in the process. From the results, most of the trends tend to increase due to the upcoming rush hour during the experiment. In addition, all the oscillations changed regularly with a period of 136 s, which is coincident with the traffic signal period and the frequency calculated by using PSD. The trends, together with the oscillations, collectively explain the most percentage of the variability of the data in the time series and provide the principal components of the data in understanding the periodic variation of the pollutant concentration. It can be deduced that vehicle emission is the major contributor to the air pollution in downtown area and pedestrians should be more alerted when crossing the busy traffic intersections. [ABSTRACT FROM AUTHOR]

In this paper we present a review of atmospheric chemistry research in China over the period 2006-2010, focusing on tropospheric ozone, aerosol chemistry, and the interactions between trace gases and aerosols in the polluted areas of China. Over the past decade, China has suffered severe photochemical smog and haze pollution, especially in North China, the Yangtze River Delta, and the Pearl River Delta. Much scientific work on atmospheric chemistry and physics has been done to address this large-scale, complex environmental problem. Intensive field experiments, satellite data analyses, and model simulations have shown that air pollution is significantly changing the chemical and physical characters of the natural atmosphere over these parts of China. In addition to strong emissions of primary pollutants, photochemical and heterogeneous reactions play key roles in the formation of complex pollution. More in-depth research is recommended to reveal the formation mechanism of photochemical smog and haze pollution and their climatic effects at the urban, regional, and global scales. [ABSTRACT FROM AUTHOR]

Accurate forecasts of fog and visibility are very important to air and high way traffic, and are still a big challenge. A 1D fog model (PAFOG) is coupled to MM5 by obtaining the initial and boundary conditions (IC/BC) and some other necessary input parameters from MM5. Thus, PAFOG can be run for any area of interest. On the other hand, MM5 itself can be used to simulate fog events over a large domain. This paper presents evaluations of the fog predictability of these two systems for December of 2006 and December of 2007, with nine regional fog events observed in a field experiment, as well as over a large domain in eastern China. Among the simulations of the nine fog events by the two systems, two cases were investigated in detail. Daily results of ground level meteorology were validated against the routine observations at the CMA observational network. Daily fog occurrences for the two study periods was validated in Nanjing. General performance of the two models for the nine fog cases are presented by comparing with routine and field observational data. The results of MM5 and PAFOG for two typical fog cases are verified in detail against field observations. The verifications demonstrated that all methods tended to overestimate fog occurrence, especially for near-fog cases. In terms of TS/ETS, the LWC-only threshold with MM5 showed the best performance, while PAFOG showed the worst. MM5 performed better for advection-radiation fog than for radiation fog, and PAFOG could be an alternative tool for forecasting radiation fogs. PAFOG did show advantages over MM5 on the fog dissipation time. The performance of PAFOG highly depended on the quality of MM5 output. The sensitive runs of PAFOG with different IC/BC showed the capability of using MM5 output to run the 1D model and the high sensitivity of PAFOG on cloud cover. Future works should intensify the study of how to improve the quality of input data (e.g. cloud cover, advection, large scale subsidence) for the 1D model, particularly how to eliminate near-fog case in fog forecasting. [ABSTRACT FROM AUTHOR]

Since the policy of 'Reform and Open to the Outside World' was implemented from 1978, urbanization has been rapid in China, leading to the expansion of urban areas and population synchronous with swift advances in economy. With urban development underway, the urban heat island (UHI) and air pollution are being enhanced, together with vegetation coverage and relative humidity on the decrease. These changes lead to: (1) decline of annual fog days in cities (e.g. In Chongqing, so-called city of fog in China, the annual fog days have reduced from 100-145 in the 1950s to about 20-30 in the 2000s); (2) decrease in fog water content (FWC) and fog droplet size, but increase in fog droplets number concentration [e.g. Jinghong, a city in Yunnan province, the average FWC (the droplet diameter) during an extremely dense fog episode with drizzle was 0.74 g/m (28.6 μm) during the 1968/69 winter and 0.08 g/m (6.8 μm) in another extremely dense fog episode during the 1986/87 winter, correspondingly, the fog droplets number density had increased from 34.9 to 153 cm]; (3) decrease in fog water deposition (FWD) (e.g. the annual mean FWD measured in Jinghong had dropped from 17.3 mm in the 1950s to 4.4 mm in the 1970s and less than 1 mm in the 1980s, and no measurable FWD now.); (4) decrease in visibility in large cities (e.g. in Chongqing, the annual average visibility had decreased from 8.2-11.8 km in the 1960s to 4.9-6.5 km in the 1980s, and around 5 km in recent years); and (5) increase in the ion concentrations and acidity in fog water in urban areas [e.g. the average total ion concentration (TIC) in the center of Chongqing was 5.5 × 10μmol/L, with mean pH value of 4.0, while the corresponding values are 9.7 × 10μmol/L and over 5.5 in its rural area]. These changes endanger all kinds of transportation and human health. This paper summarized the authors' related studies, including observations and numerical simulations to confirm the above conclusions. [ABSTRACT FROM AUTHOR]

Summary In this paper, we analyze global, direct and diffuse solar radiation data on a horizontal surface observed at stations in Shanghai, Nanjing and Hangzhou for the period of 1961 to 2000. The data include monthly averages of the daily clearness index (G/G0: the ratio of global to extraterrestrial solar radiation) and the diffuse fraction (D/G: the ratio of diffuse to global solar radiation. The present study has processed and analyzed the data, including variables or statistics of mean, and annual monthly and daily total, the diurnal variation and the frequency of daily totals of global solar radiation. A correlation between daily values of clearness index and diffuse fraction is obtained and recommended correlation equations were calculated. The annual variations and trend of yearly series are analyzed for daily global, direct and diffuse radiation on a horizontal surface, as well as for daily clearness index and diffuse fraction in Shanghai, Nanjing and Hangzhou. The results show: 1) the east China is characterized by a decrease in global and direct radiation and a little increase in diffuse radiation and a negative linear relationship was obtained between clearness index and diffuse fraction. 2) The annual variations of global, direct and diffuse radiation for Shanghai, Nanjing and Hangzhou are similar with relative low values of global and direct radiation in June due to the Meiyu period. 3) The acceleration of air pollution and decrease of relative sunshine are the possible causes for the decrease of global and direct radiation. [ABSTRACT FROM AUTHOR]