The serious air pollution problem has aroused widespread public concerns in China. Nanjing city, as one of the famous cities of China, is faced with the same situation. This research aims to investigate spatial and temporal distribution characteristics of fine particulate matter (PM2.5) and the influence of weather factors on PM2.5 in Nanjing using Spearman-Rank analysis and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Hourly PM2.5 observation data and daily meteorological data were collected from 1 April 2013 to 31 December 2015. The spatial distribution result shows that the Maigaoqiao site suffered the most serious pollution. Daily PM2.5 concentrations in Nanjing varied from 7.3 μg/m3 to 336.4 μg/m3. The highest concentration was found in winter and the lowest in summer. The diurnal variation of PM2.5 increased greatly from 6 to 10 a.m. and after 6 p.m., while the concentration exhibited few variations in summer. In addition, the concentration was slightly higher on weekends compared to weekdays. PM2.5 was found to exhibit a reversed relation with wind speed, relative humidity, and precipitation. Although temperature had a positive association with PM2.5 in most months, a negative correlation was observed during the whole period. Additionally, a high concentration was mainly brought with the wind with a southwest direction and several relevant factors are discussed to explain the difference of the impacts of diverse wind directions.
Abstract:Reclamation is capable of creating abundant land to alleviate the pressure from land shortages in China. Nevertheless, coastal reclamation can lead to severe environmental degradation and landscape fragmentation. It is quite important to monitor land use and cover change (LUCC) in coastal areas, assess coastal wetland change, and predict land use requirements. The siltation of tidal flats will result in the dynamic growth and continuous expansion of coastal areas. Therefore, the process of land change in coastal areas is different from that under the fixed terrestrial boundary condition. Cellular Automata and Multi-Agent System (CA-MAS) models are commonly used to simulate LUCC, and their advantages have been well proven under the fixed boundary condition. In this paper, we propose CA-MAS combined with a shoreline evolution forecast (CA-MAS-SEF) model to simulate the land change in coastal areas. Meanwhile, the newly increased area, because of the dynamic growth of tidal flats, is considered in the simulation process. The simulation results using the improved method are verified, and compared with observed patterns using spatial overlay. In comparison with simulation results that do not consider the expansion of tidal flats, the Kappa coefficient estimated while considering the dynamic growth of tidal flats is improved from 65.9% to 70.5%, which shows that the method presented here can be applied to simulate the LUCC in growing coastal areas.
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