ABSTRACT
Studying the changes of land use and its impacts on ecological condition in coastal areas is of great significance for understanding the evolution of the regional ecological conditions and even global change. In this study, the study area encompassed 10 provincial administrative units of China's coastal areas, covering a total of 56 cities. Based on the land use and land cover data in 1980, 1990, 1995, 2000, 2005, 2010 and 2015 and the corresponding elevation data, we assessed ecological conditions and its temporal dynamic evolution and spatial differentiation characteristics with the ecological grade index method. The effects of the elevation differentiation and land-sea gradient on the ecological condition in China's coastal areas were analyzed. The results showed that the ecological conditions of China's coastal areas were basically stable and deteriorated on the whole although partially improved from 1980 to 2015. With Hangzhou Bay as a boundary-belt, ecological conditions in southern parts were better than that in the northern parts. The ecological grade index differed significantly with the variation of elevation. The areas with elevation below 10 m were in rela-tively poor ecological condition, and the regions below 30 m had the most obvious changes of ecological conditions. Moreover, the ecological conditions increased with elevation, with a gradual turnaround trend of improvement at above 400 m. There was a gradient characteristic of the ecological grade index in China's coastal areas, showing a high-low-high pattern from land to sea. Furthermore, the maximum value of ecological condition changes appeared at a distance of 10 km to the coastline, and the values decreased with the increasing of distance to the coastline.
Subject(s)
Ecosystem , China , Cities , Oceans and SeasABSTRACT
Black carbon (BC) is an important component of atmospheric pollution and has significant impacts on air quality and human health. Choosing Shanghai city for a case study, this paper explores the statistical characteristics and spatial patterns of BC concentrations using a mobile monitoring method, which differs from traditional fixed-site observations. Land use regression (LUR) modeling was conducted to examine the determinants for on-road BC concentrations, e.g. population, economic development, traffic, etc. These results showed that the average on-road BC concentrations were (9.86±8.68) µg·m-3, with a significant spatial variation. BC concentrations in suburban areas[(10.47±2.04) µg·m-3] were 32.03% (2.54 µg·m-3) higher than those in the city center[(7.93±2.79) µg·m-3]. Besides, meteorological factors (e.g. wind speed and relative humidity) and traffic variables (e.g. the length of roads, distance to provincial roads, distance to highway) had significant effects on on-road BC concentrations (r:0.5-0.7, P<0.01). Moreover, the LUR model, including meteorological and traffic variables performed well (adjusted R2:0.62-0.75, cross validation R2:0.54-0.69, RMSE:0.15-0.20 µg·m-3), which demonstrates that on-road BC concentrations in Shanghai are mainly affected by these factors and traffic sources to some extent. Among them, the most accurate LUR model was developed with a 100 m buffer, followed by the LUR model with a 5 km buffer. This study is of great significance for the identification of spatial distribution patterns for on-road BC concentration and exploring their influencing factors in Shanghai, which can provide a scientific basis and theoretical support for simulating and predicting the response mechanisms of BC on human health and the natural environment.
ABSTRACT
Gross primary productivity (GPP) plays an important role in global carbon cycle. Vegetation maximum light use efficiency (Δmax) is the key parameter for GPP simulation of terrestrial ecosystem. Based on the vegetation photosynthesis model (VPM) and the eddy covariance flux data at 40 stations from FLUXNET (179 site-years of data), we identified the key model parameters influencing the simulation of GPP with VPM through one-at-a-time (OAT) method. The cross validation method was employed to optimize the key model parameters and evaluate the model perfor-mance for global forest ecosystems. The results showed that the prediction of GPP was mostly affec-ted by Δmax, maximum temperature for photosynthesis (Tmax), and optimum temperature for photosynthesis (Topt). There were distinguishable differences for the key optimized parameters among different forest ecosystems. The optimized Δmax ranged from 0.05 to 0.08 µmol CO2·µmol-1 PAR (evergreen broad-leaved forest>evergreen coniferous forest>mixed forest>deciduous broad-leaved forest). The optimized Tmax ranged from 38 to 48 â,while Topt ranged from 18 to 22 â. With the optimized key parameters based on ecosystem types, the VPM was able to simulate the seasonal and inter-annual variations of GPP in four forest ecosystems.
