Assessing the ecological risk induced by PM2.5 pollution in a fast developing urban agglomeration of southeastern China

A Framework for a Hazard Taxonomy to Support Risk Assessment of Tangible Outdoor Heritage

“…As a result of this screening, 27 papers were discarded, while for the remaining 19, an analytic study of the portion relating to the definition of the risk was performed. The SLR made it possible to define indicators and indices of use for a quantitative assessment of the specific risk Deforestation/land conversion [73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91] in order to implement it in the prepared framework. Although CC was indicated as the main cause, the scientific community is increasingly aware of the role played by anthropic pressures on the natural ecosystems.…”

Scientometric and multidimensional contents analysis of PM2.5 concentration prediction

“…(2) Natural condition indicators, topographic condition data include: Enhanced Vegetation Index (EVI), Digital elevation model (DEM), and Normalized Vegetation Index (NDVI); Meteorological conditions include: relative humidity (RHU), mean pressure (PRS), mean temperature (TEM), gradient Surface Temperature (GST), mean wind speed (WIN–S), wind direction (WIN-D), visibility (VIS), planetary boundary layer height (PBL), dew point temperature (DPT), atmospheric stability etc. [ 7 , 9 , 77 ]. (3) Relevant interfering factors: NO 2 , SO 2 , CO, and O 3 can not only represent the emissions of primary pollutants during the formation of PM 2.5 heavy pollution but also are precursors and oxidants for the transformation of secondary pollutants, so they can be used as auxiliary variables to predict PM 2.5 concentrations [ 7 , 62 ].…”

“…Medium-term scale (monthly and seasonal) forecasts can provide a basis for seasonal production formulation adjustment and pollution prevention and control in the industrial sector. Long-term scale (annual) PM 2.5 concentration prediction can provide a basis for regional environmental planning and national economic development planning target setting [ 3 , 9 ].…”

“…In terms of research scales, the microscale needs to be deepened and the macroscale needs to be strengthened, especially the medium- and long-term macroscale PM 2.5 concentration prediction by multi-source data fusion is increasingly in the spotlight. For example, based on PM 2.5 concentration data from satellite remote sensing inversion, combined with climate change, land use planning information and social development multi-source data under different Representative Concentration Pathways (RCP) scenarios, machine learning algorithms are used to scientifically predict the annual average PM 2.5 concentration distribution at 1 km resolution under different RCP scenarios [ 9 ]. Another example could be the implementation of China’s dual carbon strategy.…”

“…Due to the diverse sources of PM 2.5 pollution components and the complexity of influencing factors, there are highly nonlinear relationships among pollution indicators, so it is difficult to establish accurate prediction models by traditional mathematical modeling methods. While a large amount of data collection and analysis of driving mechanisms are required, which, together with the interference of real-time changes in meteorological conditions, increases the difficulty of PM 2.5 concentration prediction [ 9 , 10 ]. Recently, as the fundamental air quality data continue to improve and prediction techniques become more diverse, the research scope of PM 2.5 concentration prediction is also expanding.…”