Spatial-temporal characteristics of PM<Subscript>2.5</Subscript> in China: A city-level perspective analysis

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China’s PM_2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM_2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM_2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM_2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

     

Water content and land use history controlling soil CO2 respiration and carbon stock in savanna soil and groundnut fields in semi-arid Senegal

Decomposition of soil organic carbon (SOC) regulates the partitioning between soil C-stock and release of CO₂ to the atmosphere and is vital for soil fertility. Agricultural expansion followed by decreasing amounts of SOC and soil fertility is a problem mainly seen in tropical agro-ecosystems where fertilizers are in short supply. This paper focuses on factors influencing temporal trends in soil respiration measured as CO₂ effluxes in grass savanna compared with groundnut (Arachis hypogaea L.) fields in the semi-arid part of Senegal in West Africa. Based on laboratory experiments, soil CO₂ production has been expressed as a function of temperature and soil water content by fit equations. Field measurements included soil CO₂ effluxes, soil temperatures and water contents. Effluxes in grass savanna and groundnut fields during the dry season were negligible, while effluxes during the rainy season were about 3–8 μmol CO₂ m^?2 s^?1, decreasing to less than 1 μmol by the end of the growing season. Annual soil CO₂ production was simulated to be in the range of 31–38 mol C m^?2. Furthermore, a controlled water addition experiment revealed the importance of rain during the dry season for the overall turnover of soil organic matter.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

Climate spatial variability and data resolution in a semi-arid watershed, south-eastern Arizona

In the 10,000 km² San Pedro River watershed area in south-eastern Arizona, high-resolution spatial patterns of long-term precipitation and temperature were better reproduced by kriging climate data with elevation as external drift (KED) than by multiple linear regression on station location and elevation as judged by the spatial distribution of interpolation error. Mean errors were similar overall, and interpolation accuracy for both methods increased with increasing correlation between climate variables and elevation. Uncertainty in station locations had negligible effect on mean estimation error, although error for individual stations varied as much as 27%. Our future ability to examine spatial aspects of climate change at high spatial resolution will be severely limited by continuing closures of climate stations in this part of the United States.     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.     

A HIGH-RESOLUTION, GRIDDED DATASET FOR MONTHLY TEMPERATURE NORMALS (1971-2000) IN SWEDEN

A baseline climatology is required in evaluating climate variability and changes on regional and local scales. Gridded climate normals, i.e. averages over a 30‐year period, are of special interest since they can be readily used for validation of climate models. This study is aimed at creating an updated gridded dataset for Swedish monthly temperature normals over the period 1971–2000, based on standard 2‐m air temperature records at 510 stations in mainland Sweden. Spatial trends of the normal temperatures were modelled as functions of latitude, longitude and elevation by multiple linear regression. The study shows that the temperature normals are strongly correlated with latitude throughout the year and especially in cold months, while elevation was a more important factor in June and July. Longitude played a minor role and was only significant in April and May. Regression equations linking temperature to latitude, longitude and elevation were set up for each month. Monthly temperature normals were detrended by subtracting spatial trends given by the regressions. Ordinary kriging was then applied to both original data (simple method) and de‐trended data (composite method) to model the spatial variability and to perform spatial gridding. The multiple regressions showed that between 82% (summer) and 96% (winter) of the variance in monthly temperature normals could be explained by latitude and elevation. Unexplained variances, i.e. the residuals, were modelled with ordinary kriging with exponential semivariograms. The composite grid estimates were calculated by adding the multiple linear trends back to the interpolated residuals at each grid point. Kriged original temperature normals provided a performance benchmark. The cross–validation shows that the interpolation errors of the normals are significantly reduced if the composite method rather than the simple one was used. A gridded monthly dataset with 30‐arcsecond spacing was created using the established trends, the kriging model and a digital topographic dataset.     

An optimized spatial proximity model for fine particulate matter air pollution exposure assessment in areas of sparse monitoring

GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants.

Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM_2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM_2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R² between possible exposure intensity calculated by GWFPM and observed PM_2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM_2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse.     

Investigation of Chemical Composition of Atmospheric Aerosol in Ulaanbaatar During 2005–2014

Results from investigating atmospheric aerosols in Ulaanbaatar (Mongolia) 2005–2014 are presented. It is found that the largest sources of suspended particles are represented by fuel combustion and the concomitant exhaust gases, thermal power generation, industrial facilities and mineral dust transport; the main components of aerosol particles are SO ₄ ^2- , NO ₃ ^- , Cl^–, NH ₄ ⁺ , Ca²⁺ and Na⁺. It is determined that in the wintertime, as a result of an increase in fuel consumption, calm weather and orographic characteristics of topography which contribute to accumulation of contaminants in the urban atmosphere, the average sum of ions increases to 43.9–114.6 μg/m³ against 7.44–18.48 μg/m³ during a warm season. Interannual differences in aerosol composition were noted; the total ion content averaged 43.9 μg/m³ during December 2011, 114.6 μg /m³ during December 2012, 68.7 μg/m³ during December 2013, and 64.7 μg/m³ during December 2014. It was found that the concentration of the sum of ions in the aerosol during the winter period is by a factor of 6 higher than during the summer. The highest exceedance of the concentrations was observed for the alkaline earth ions Na⁺, K⁺, Ca²⁺ and Mg²⁺ (by a factor of more than 20) forming part of the ash components. There is a difference in the chemical composition of the aerosol sampled in the center of the city and on its outskirts. It is established that the aerosol composition and concentration in Ulaanbaatar during the winter period are comparable the aerosol composition in the industrial cities of China.     

Modeling Short-Term Spatial and Temporal Variability of Groundwater Level Using Geostatistics and GIS

Continuous depletion of groundwater levels from deliberate and uncontrolled exploitation of groundwater resources lead to the severe problems in arid and semi-arid hard-rock regions of the world. Geostatistics and geographic information system (GIS) have been proved as successful tools for efficient planning and management of the groundwater resources. The present study demonstrated applicability of geostatistics and GIS to understand spatial and temporal behavior of groundwater levels in a semi-arid hard-rock aquifer of Western India. Monthly groundwater levels of 50 sites in the study area for 36-month period (May 2006 to June 2009; excluding 3 months) were analyzed to find spatial autocorrelation and variances in the groundwater levels. Experimental variogram of the observed groundwater levels was computed at 750-m lag distance interval and the four most-widely used geostatistical models were fitted to the experimental variogram. The best-fit geostatistical model was selected by using two goodness-of-fit criteria, i.e., root mean square error (RMSE) and correlation coefficient (r). Then spatial maps of the groundwater levels were prepared through kriging technique by means of the best-fit geostatistical model. Results of two spatial statistics (Geary’s C and Moran’s I) indicated a strong positive autocorrelation in the groundwater levels within 3-km lag distance. It is emphasized that the spatial statistics are promising tools for geostatistical modeling, which help choose appropriate values of model parameters. Nugget-sill ratio (<0.25) revealed that the groundwater levels have strong spatial dependence in the area. The statistical indicators (RMSE and r) suggested that any of the three geostatistical models, i.e., spherical, circular, and exponential, can be selected as the best-fit model for reliable and accurate spatial interpolation. However, exponential model is used as the best-fit model in the present study. Selection of the exponential model as the best-fit was further supported by very high values of coefficient of determination (r ² ranging from 0.927 to 0.994). Spatial distribution maps of groundwater levels indicated that the groundwater levels are strongly affected by surface topography and the presence of surface water bodies in the study area. Temporal pattern of the groundwater levels is mainly controlled by the rainy-season recharge and amount of groundwater extraction. Furthermore, it was found that the kriging technique is helpful in identifying critical locations over the study area where water saving and groundwater augmentation techniques need to be implemented to protect depleting groundwater resources.     

Spatial precipitation and evapotranspiration in the typical steppe of Inner Mongolia,China – A model based approach using MODIS data

We present a simple method to derive spatial precipitation (P) and evapotranspiration (ET) for the typical steppe of the Xilin river catchment at 1 km and 8-day resolution during the main vegetation period (23 April to 28 August) of 2006. The hydrological model BROOK90 was parameterised from eddy covariance measurements. The daily model input data, precipitation, minimum (Ta_min) and maximum air temperature (Ta_max), were derived by manipulating MODIS leaf area index (LAI) and surface temperature data. P was estimated based on a linear regression of P measured at several sites against the mean gain of the MODIS LAI of surrounding 3 × 3 pixels areas (R² = 0.76). Ta_min and Ta_max were derived using a relationship between measured Ta_min and Ta_max and MODIS surface temperatures (R² = 0.92 and R² = 0.88, respectively). The mean precipitation was 145 mm; it varied between 52 mm in the north-western region and 239 mm in the eastern region. In spring, the modelled ET was low (<0.8 mm d⁻¹); evaporation dominated over transpiration and spatial differences were small. At the end of June, the mean ET reached its maximum (2 mm d⁻¹) and spatial differences were pronounced. From July on, transpiration dominated over declining evaporation, and spatial differences decreased in August.     

Very high CO2 exchange fluxes at the peak of the rainy season in a West African grazed semi-arid savanna ecosystem

Africa is a sink of carbon, but there are large gaps in our knowledge regarding the CO₂ exchange fluxes for many African ecosystems. Here, we analyse multi-annual eddy covariance data of CO₂ exchange fluxes for a grazed Sahelian semi-arid savanna ecosystem in Senegal, West Africa. The aim of the study is to investigate the high CO₂ exchange fluxes measured at the peak of the rainy season at the Dahra field site: gross primary productivity and ecosystem respiration peaked at values up to ?48 μmol CO₂ m^?2 s^?1 and 20 μmol CO₂ m^?2 s^?1, respectively. Possible explanations for such high fluxes include a combination of moderately dense herbaceous C4 ground vegetation, high soil nutrient availability and a grazing pressure increasing the fluxes. Even though the peak net CO₂ uptake was high, the annual budget of ?229 ± 7 ± 49 g C m^?2 y^?1 (±random errors ± systematic errors) is comparable to that of other semi-arid savanna sites due the short length of the rainy season. An inter-comparison between the open-path and a closed-path infrared sensor indicated no systematic errors related to the instrumentation. An uncertainty analysis of long-term NEE budgets indicated that corrections for air density fluctuations were the largest error source (11.3% out of 24.3% uncertainty). Soil organic carbon data indicated a substantial increase in the soil organic carbon pool for the uppermost .20 m. These findings have large implications for the perception of the carbon sink/source of Sahelian ecosystems and its response to climate change.     

基于GIS的新疆气温数据栅格化方法研究

以新疆99个气象台站1971-2010年年平均气温为数据源,采用多元回归结合空间插值的方法对新疆区域气温数据进行栅格化研究。建立了年平均气温与台站经纬度和海拔高度的多元回归模型,对于残差数据的插值采用了反距离权重法(IDW) 、普通克立格法 (Kriging)和样条函数法(Spline)3种目前应用广泛的空间插值方法,针对于这3种方法进行了基于MAE和RMSIE的交叉验证和对比分析,结果表明在新疆的年平均气温的GIS插值方案中,IDW方法精度总体要高于其他两种插值方法。     

A high-resolution pigment and productivity record from the varved Ponte Tresa basin (Lake Lugano,Switzerland) since 1919: insight from an approach that combines hyperspectral imaging and high-performance liquid chromatography

Eutrophication, prompted by anthropogenic activities and climate change has led to multiple adverse effects in freshwater systems across the world. As instrumental measurements are typically short, lake sediment proxies of aquatic primary productivity (PP) are often used to extend the observational record of eutrophication back in time. Sedimentary pigments provide specific information on PP and major algal communities, but the records are often limited in the temporal resolution. Hyperspectral imaging (HSI) data, in contrast, provide very high seasonal (sub-varve-scale) resolution, but the pigment speciation is limited. Here, we explore a combined approach on varved sediments from the Ponte Tresa basin, southern Switzerland, taking the advantages of both methods (HSI and high performance liquid chromatography, HPLC) with the goal to reconstruct the recent eutrophication history at seasonal to interannual resolution. We propose a modified scheme for the calibration of HSI data (here: Relative Absorption Band Depth between 590 and 730 nm RABD_590–730) and HPLC-inferred pigment concentrations (here: ‘green pigments’ {chlorophyll a and pheophytin a}) and present a calibration model (R²?=?0.82; RMSEP?~?12%). The calibration range covers >?98% of the spectral index values of all individual pixels (68 µm?×?68 µm) in the sediment core. This allows us to identify and quantify extreme pigment concentrations related to individual major algal blooms, to identify multiple algal blooms within one season, and to assess interannual variability of PP. Prior to the 1930s, ‘green pigment’ concentrations and fluxes (~?50 µg g^?1;?~?2 µg cm^?2a^?1, chlorophyll a and pheophytin a) and interannual variability was very low. From the 1930s to 1964, chlorophyll a and pheophytin a increased by a factor of ~?4, and ββ-carotene appeared in substantial amounts (~?0.4 µg cm^?2a^?1). Interannual variability increased markedly and a first strong algal bloom with ‘green pigment’ concentrations as high as 700 µg g^?1 is observed in 1958. Peak eutrophication (~?12 µg cm^?2a^?1 chlorophyll a and pheophytin a) and very high interannual variability with extreme algal blooms (‘green pigment’ concentrations up to 1400 µg g^?1) is observed until ca. 1990, when eutrophication decreases slightly. Maximum PP values after 2009 are likely the result of internal nutrient cycling related to repeated deep mixing of the lake.     

近50年中国光合有效辐射的时空变化(英文)

Based on long-term measurement data of weather/ecological stations over China,this paper calculated and produced annually-and seasonally-averaged Photosynthetically Active Radiation(PAR) spatial data from 1961 to 2007,using climatological calculations and spatialization techniques.The spatio-temporal variation characteristics of annually-and seasonally-averaged PAR spatial data over China in recent 50 years were analyzed with Mann-Kendall trend analysis method and GIS spatial analysis techniques.The results show that:(1) As a whole,the spatial distribution of PAR is complex and inhomogeneous across China,with lower PAR in the eastern and southern parts of China and higher PAR in the western part.Mean annual PAR over China ranges from 17.7 mol m-2 d-1 to 39.5 mol m-2 d-1.(2) Annually-and seasonally-averaged PAR of each pixel over China are averaged as a whole and the mean values decline visibly with fluctuant processes,and the changing rate of annually-averaged PAR is-0.138 mol m-2 d-1/10a.The changing amplitudes among four seasons are different,with maximum dropping in summer,and the descending speed of PAR is faster before the 1990s,after which the speed slows down.(3) The analysis by each pixel shows that PAR declines significantly(α=0.05) in most parts of China.Summer and winter play more important roles in the interannual variability of PAR.North China is always a decreasing zone in four seasons,while the northwest of Qinghai-Tibet Plateau turns to be an increasing zone in four seasons.(4) The spatial distributions of the interannual variability of PAR vary among different periods.The interannual variabilities of PAR in a certain region are different not only among four seasons,but also among different periods.     

中国城市群PM2.5空间分布规律（英文）

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_(2.5) makes a significant contribution to poor air quality. The spatio-temporal features of China's PM_(2.5) concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification(ISIC), reveals the spatio-temporal variations of PM_(2.5) concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_(2.5) concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_(2.5) concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_(2.5) concentrations. Results:(1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China.(2) The PM_(2.5) concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change.(3) PM_(2.5) concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

陕西省榆林市秋冬季黑碳观测研究

利用2011年秋冬季榆林大气成分站黑碳浓度、颗粒物质量浓度、大气能见度、地面气象资料,计算边界层高度、气溶胶吸收系数、大气消光系数,导出单次散射反照率,并对其进行分析讨论。结果表明：（1） 榆林秋冬季平均黑碳浓度为2.6 μg·m^-3。（2）黑碳占颗粒物质量浓度PM_1.0比值为10.6%,黑碳与颗粒物质量浓度PM_1.0、PM_2.5、PM₁₀相关系数分别为0.91、0.91、0.72。（3）黑碳浓度受边界层高度影响,沙漠风场对黑碳的堆积输送起主导作用。（4） 榆林地区气溶胶吸收系数与大气消光系数比值为16.8%。（5）单次散射反照率平均值为0.72。     

Validation of WRF model on simulating forcing data for Heihe River Basin

The research of coupling WRF (Weather Research and Forecasting Model) with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA (China Meteorological Administration) and hourly meteorological observation data from seven sites of WATER (Watershed Airborne Telemetry Experimental Research) are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE (Mean Bias Error) of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were ?0.19 °C, ?4.49 hPa, 4.08% and 0.92 m/s, the average RMSE (Root Mean Square Error) of them were 2.11 °C, 5.37 hPa, 9.55% and 1.73 m/s, and the average R (correlation coefficient) of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were ?0.16 °C, ?6.62 hPa, ?5.14%, 0.26 m/s, 33.0 W/m² and ?6.44 W/m², the average RMSE of them were 2.62 °C, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m² and 53.5 W/m², and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained: (1) regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; (2) the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; (3) both wind speed and rainfall simulated from WRF model did not agree well with observation data.     

典型岩溶农业区地下水质与土地利用变化: 以云南小江流域为例

1 Introduction Ecological environment such as water, soil, etc. are very fragile in the karst area because of the special geological background. With the fast increase of the population and rapid social and economic development in karst area, contradictio…     

Synergic effect of salinity and light-chilling on photosystem II photochemistry of the halophyte,Sarcocornia fruticosa

Laboratory experiments were conduced to assess the synergic effect of chilling and light on photosystem II photochemistry of the halophyte, Sarcocornia fruticosa, grown at different salinity concentrations (0, 170, 340, 510 and 1030 mM). Chlorophyll fluorescence was measured after chilling (at 5 °C in darkness) and light-chilling (at 5 °C and 700 μmol m^?2 s^?1) treatments, and after 24 h of recovery (at 20 °C and 75 μmol m^?2 s^?1). At 5 °C and 700 μmol m^?2 s^?1, plants grown with 0 and 170 mM NaCl showed the lowest F_v/F_m values, whereas quantum efficiency of PSII (Φ_PSII) was higher for plants grown at 170 and 340 mM NaCl, these results being consistent after two exposures to chilling treatments. Susceptibility to photoinhibition decreases when low temperature and high light are combined with high salinity. Therefore, populations of S. fruticosa that occur in arid environments with salinities c. 340 mM could show a higher tolerance to light-chilling.