Feng Zhou

Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods.

Various multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA), factor analysis (FA), and principal component analysis (PCA) were used to explain the spatial and temporal patterns of surface water pollution in Lake Dianchi. The dataset, obtained during the period 2003–2007 from the Kunming Environmental Monitoring Center, consisted of 12 variables surveyed monthly at eight sites. The CA grouped the 12xa0months into two groups, August–September and the remainder, and divided the lake into two regions based on their different physicochemical properties and pollution levels. The DA showed the best results for data reduction and pattern recognition in both temporal and spatial analysis. It calculated four parameters (TEMP, pH, CODMn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH

Scientometric analysis of geostatistics using multivariate methods

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The contribution of China's emissions to global climate forcing.

–N, and TN) to almost 71.7% correct assignment in spatial analysis of the two clusters. The FA/PCA applied to datasets of two special clusters of the lake calculated four factors for each region, capturing 72.5% and 62.5% of the total variance, respectively. Strong loadings included DO, BOD, TN, CODCr, CODMn, NH

A worldwide analysis of spatiotemporal changes in water balance‐based evapotranspiration from 1982 to 2009

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Enhanced-interval linear programming

–N, TP, and EC. In addition, box–whisker plots and GIS further facilitated and supported the multivariate analysis results.

A New High-Resolution N2O Emission Inventory for China in 2008

Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author, for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Knowledge of the contribution that individual countries have made to global radiative forcing is important to the implementation of the agreement on “common but differentiated responsibilities” reached by the United Nations Framework Convention on Climate Change. Over the past three decades, China has experienced rapid economic development, accompanied by increased emission of greenhouse gases, ozone precursors and aerosols, but the magnitude of the associated radiative forcing has remained unclear. Here we use a global coupled biogeochemistry–climate model and a chemistry and transport model to quantify China’s present-day contribution to global radiative forcing due to well-mixed greenhouse gases, short-lived atmospheric climate forcers and land-use-induced regional surface albedo changes. We find that China contributes 10%u2009±u20094% of the current global radiative forcing. China’s relative contribution to the positive (warming) component of global radiative forcing, mainly induced by well-mixed greenhouse gases and black carbon aerosols, is 12%u2009±u20092%. Its relative contribution to the negative (cooling) component is 15%u2009±u20096%, dominated by the effect of sulfate and nitrate aerosols. China’s strongest contributions are 0.16u2009±u20090.02 watts per square metre for CO2 from fossil fuel burning, 0.13u2009±u20090.05 watts per square metre for CH4, −0.11u2009±u20090.05 watts per square metre for sulfate aerosols, and 0.09u2009±u20090.06 watts per square metre for black carbon aerosols. China’s eventual goal of improving air quality will result in changes in radiative forcing in the coming years: a reduction of sulfur dioxide emissions would drive a faster future warming, unless offset by larger reductions of radiative forcing from well-mixed greenhouse gases and black carbon.

Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100

A satellite-based water balance method is developed to model global evapotranspiration (ET) through coupling a water balance (WB) model with a machine-learning algorithm (the model tree ensemble, MTE) (hereafterWB-MTE). TheWB-MTE algorithmwas firstly trained by combiningmonthlyWB-estimated basin ET with the potential drivers (e.g., radiation, temperature, precipitation, wind speed, and vegetation index) across 95 large river basins (5824 basin-months) and then applied to establish global monthly ETmaps at a spatial resolution of 0.5° from 1982 to 2009. The global land ET estimated from WB-MTE has an annual mean of 593±17mm for 1982–2009, with a spatial distribution consistent with previous studies in all latitudes but the tropics. The ET estimated by WB-MTE also shows significant linear trends in both annual and seasonal global ET during 1982–2009, though the trends seem to have stalled after 1998. Moreover, our study presents a striking difference from the previous ones primarily in the magnitude of ET estimates during the wet season particularly in the tropics, where ET is highly uncertain due to lack of direct measurements. This may be tied to their lack of proper consideration to solar radiation and/or the rainfall interception process. By contrast, in the dry season, our estimate of ET compares well with the previous ones, both for the mean state and the variability. If we are to reduce the uncertainties in estimating ET, these results emphasize the necessity of deploying more observations during the wet season, particularly in the tropics.

New model for capturing the variations of fertilizer‐induced emission factors of N2O

An enhanced-interval linear programming (EILP) model and its solution algorithm have been developed that incorporate enhanced-interval uncertainty (e.g., A±, B± and C±) in a linear optimization framework. As a new extension of linear programming, the EILP model has the following advantages. Its solution space is absolutely feasible compared to that of interval linear programming (ILP), which helps to achieve insight into the expected-value-oriented trade-off between system benefits and risks of constraint violations. The degree of uncertainty of its enhanced-interval objective function (EIOF) would be lower than that of ILP model when the solution space is absolutely feasible, and the EIOFs expected value could be used as a criterion for generating the appropriate alternatives, which help decision-makers obtain non-extreme decisions. Moreover, because it can be decomposed into two submodels, EILPs computational requirement is lower than that of stochastic and fuzzy LP models. The results of a numeric example further indicated the feasibility and effectiveness of EILP model. In addition, EI nonlinear programming models, hybrid stochastic or fuzzy EILP models as well as risk-based trade-off analysis for EI uncertainty within decision process can be further developed to improve its applicability.

Influence of anthropogenic aerosol deposition on the relationship between oceanic productivity and warming

The amount and geographic distribution of N2O emissions over China remain largely uncertain. In this study, county-level and 0.1° × 0.1° gridded anthropogenic N2O emission inventories for China (PKU-N2O) in 2008 are developed based on high-resolution activity data and regional emission factors (EFs) and parameters. These new estimates are compared with previous inventories, and with two sensitivity tests: one that uses high-resolution activity data but the default IPCC methodology (S1) and the other that uses regional EFs and parameters but starts from coarser-resolution activity data. The total N2O emissions are 2150 GgN2O/yr (interquartile range from 1174 to 2787 GgN2O/yr). Agriculture contributes 64% of the total, followed by energy (17%), indirect emissions (12%), wastes (5%), industry (2.8%), and wildfires (0.2%). Our national emission total is 17% greater than that of the EDGAR v4.2 global product sampled over China and is also greater than the GAINS-China, NDRC, and S1 estimates by 10%, 50%, and 17%, respectively. We also found that using uniform EFs and parameters or starting from national/provincial data causes systematic spatial biases compared to PKU-N2O. Spatial analysis shows nonlinear relationships between N2O emission intensities and urbanization. Per-capita and per-GDP N2O emissions increase gradually with an increase in the urban population fraction from 0.3 to 0.9 among 2884 counties, and N2O emission density increases with urban expansion.