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Marine spatial population dynamics are often addressed with a focus on larval dispersal, without taking into account movement behavior of individuals in later life stages. Processes occurring during demersal life stages may also drive spatial population dynamics if habitat quality is perceived differently by animals belonging to different life stages. In this study, we used a dual approach to understand how stage‐structured habitat use and dispersal ability of adults shape the population of a marine fish species. Our study area and focal species provided us with the unique opportunity to study a closed island population. A spatial simulation model was used to estimate dispersal distances along a coral reef that surrounds the island, while contributions of different nursery bays were determined based on otolith stable isotope signatures of adult reef fish. The model showed that adult dispersal away from reef areas near nursery bays is limited. The results further show that different bays contributed unequally to the adult population on the coral reef, with productivity of juveniles in bay nursery habitat determining the degree of mixing among local populations on the reef and with one highly productive area contributing most to the island's reef fish population. The contribution of the coral reef as a nursery habitat was minimal, even though it had a much larger surface area. These findings indicate that the geographic distribution of nursery areas and their productivity are important drivers for the spatial distribution patterns of adults on coral reefs. We suggest that limited dispersal of adults on reefs can lead to a source–sink structure in the adult stage, where reefs close to nurseries replenish more isolated reef areas. Understanding these spatial population dynamics of the demersal phase of marine animals is of major importance for the design and placement of marine reserves, as nursery areas contribute differently to maintain adult populations.

As part of the 12th Five-Year Plan, the Chinese government has developed air pollution prevention and control plans for key regions with a focus on the power, transport, and industrial sectors. Here, we investigate the contribution of residential emissions to regional air pollution in highly polluted eastern China during the heating season, and find that dramatic improvements in air quality would also result from reduction in residential emissions. We use the Weather Research and Forecasting model coupled with Chemistry to evaluate potential residential emission controls in Beijing and in the Beijing, Tianjin, and Hebei (BTH) region. In January and February 2010, relative to the base case, eliminating residential emissions in Beijing reduced daily average surface PM2.5 (particulate mater with aerodynamic diameter equal or smaller than 2.5 micrometer) concentrations by 14 ± 7 μg·m−3 (22 ± 6% of a baseline concentration of 67 ± 41 μg·m−3; mean ± SD). Eliminating residential emissions in the BTH region reduced concentrations by 28 ± 19 μg·m−3 (40 ± 9% of 67 ± 41 μg·m−3), 44 ± 27 μg·m−3 (43 ± 10% of 99 ± 54 μg·m−3), and 25 ± 14 μg·m−3 (35 ± 8% of 70 ± 35 μg·m−3) in Beijing, Tianjin, and Hebei provinces, respectively. Annually, elimination of residential sources in the BTH region reduced emissions of primary PM2.5 by 32%, compared with 5%, 6%, and 58% achieved by eliminating emissions from the transportation, power, and industry sectors, respectively. We also find air quality in Beijing would benefit substantially from reductions in residential emissions from regional controls in Tianjin and Hebei, indicating the value of policies at the regional level.

There are fan noise and transformer noise sources inside the converter cabin which may bring the environmental noise pollution through airborne or structural borne paths. In order to identify the contribution of each source and its path, transfer path analysis test was performed. Take full load working condition at full speed as an example, the contributions of fan air borne noise, fan structural borne noise, transformer air borne noise and transformer structural borne noise are 88.33%, 3.79%, 7.37% and 0.51% respectively. The contribution of air borne noise is 95.70% more than that of structural borne noise which is 4.30%. In the end the aerodynamic noise simulation of converter cabin is compared with the experimental result which is in good coincidence, this result can also verifies the noise source contribution result.

Provenance analysis of aeolian sediments is key for understanding its origin, transportation and climatic significance. The ability to identify and quantify source contributions of aeolian sediments remains challenging. This study applies a discriminant function analysis (DFA) method to analyze the geochemistry of five different types of sediments (mobile dunes, vegetated dunes, fluvial sands, sandy loess and loess) in the Taklamakan desert and quantify the relative contribution of sediment sources. The geochemical compositions of sediments are influenced by the source rock compositions, chemical weathering and grain size. We analysed the effects of these three factors on the geochemical characteristics of sediments in detail and identified seven source tracers. The seven tracers of potential source areas and target sediments were compared by DFA, to obtain a series of discriminant functions which could identify the sources and their relative contributions. The results show that the aeolian sediments in the Taklamakan Desert are primarily sourced from the Kunlun Mountains and the Tianshan Mountains, with different grain sizes having different sources. The provenance of different types of sediments within the same grain size fractions were found to remain unchanged. The contribution of sediments sourced from the Kunlun Mountain and Tianshan Mountain to the > 63 μm fractions are 60% and 40% respectively, while the contribution to the < 63 μm fractions are 40% and 60% respectively. The findings are valuable for further understanding the source and transport mechanism of sediment in arid and semi-arid areas.

Summer streamflow variations strongly affect water supply reliability and ecological functioning of western U.S. (WUS) streams. Traditional snow‐based forecasts of summer streamflow are becoming less accurate with warming‐induced reductions in winter snow accumulation. This reflects a rising importance of competing runoff‐generating processes in controlling summer streamflow variations, primarily an increasing role of rainfall in contrast to snowmelt. Here, based on a snowmelt‐rainfall tracking algorithm applied to two hydrological models, we show that cool‐season rainfall provides an important volumetric contribution to summer streamflow for many WUS streams in the current climate, and this contribution will increase under climate warming, especially in years with warm snow droughts and abnormally dry summers. We also show that seasonal rainfall (warm‐/cool‐seasons) dominates the variability of summer streamflow across ∼70% area of WUS. We show that an increasing warm‐season rainfall contribution to summer streamflow (largely replacing snowmelt) results in reduced summer streamflow predictability. Plain Language Summary Summer streamflow is a critical water resource in the generally dry summers of the western U.S. (WUS), and is routinely forecasted using spring snowpack and/or winter total precipitation as primary predictors. However, climate warming leads to reduced snowpacks, exacerbates summer low flows, and reduces the accuracy of snow‐based summer streamflow forecasts. On the other hand, the role of winter rainfall as a control on summer streamflow increases in a warmer climate. Here, we explicitly quantify the contributions from cool‐season rainfall, warm‐season rainfall, and snowmelt to summer streamflow across the WUS, and how they change under a uniformly 1°C warmer climate. We show that the cool‐season rainfall contribution to summer streamflow increases under warming across WUS, especially in streams that currently have low‐to‐moderate snow contributions to runoff, and in years with anomalously warm winters and/or dry summers. We also show that the warm‐season rainfall contribution to summer streamflow increases widely, especially in the southern interior of WUS in a warmer climate, and that increasing warm‐season rainfall contribution to summer streamflow (largely replacing snowmelt) results in reduced summer streamflow predictability. Key Points The cool‐season rainfall contribution to summer streamflow is greatest in low‐elevation coastal streams with dry summers Climate warming leads to an increased contribution of seasonal rainfall to summer streamflow as spring snowmelt contributions decline Summer streamflow predictability declines with reduced snowmelt and increased warm‐season rainfall contribution in a warmer climate

BackgroundThe chemical composition of precipitation was assessed in 27 European countries from 2000 to 2017, offering a general point of view on the rainwater chemistry in Europe, contributing to a larger understanding of air pollution and atmospheric chemistry.ResultsThe volume-weighted mean concentrations (VWM) were calculated, showing the relative dominance of SO42− and Cl−, explaining the acidic and slightly acidic pH values that ranged from 4.19 to 5.82 over Europe. The VWM concentrations of ionic species measured in rainwater usually followed the SO42− > Cl− > Na+ > NH4+ > NO3− > H+ > Ca2+ > Mg2+ > K+ > HCO3− downward order, with small exceptions, depending on the industrial activity or legal framework of a particular area. Fractional acidity showed that ~ 69% of the inorganic acidity in precipitation is neutralised, whilst neutralisation factors showed that Na+ and NH4+ contributed the most to the neutralising process. The relationship between acidic and neutralising compounds was further examined by calculating the ionic ratios. Wet deposition rates showed the dominance of acidic compounds over neutralising ones, reflecting the climatic influences and the local and regional economic characteristics of different regions from Europe. Origins of major ions in rainwater were examined using the sea salt and non-sea salt fractions, crustal and marine enrichment factors, correlation analysis and the Principal Component Analysis, showing the significant influence of anthropogenic sources (industry, agriculture, traffic). Natural sources (maritime, terrestrial) also play a major role in influencing the precipitation chemistry of the European continent.ConclusionsThe main conclusion of this study regarding the precipitation chemistry of the European continent during the studied period is represented by the relative homogenous distribution of the analysed chemical species, which is most likely due to the unitary economic development and to the implementation of common European policies in the field of environmental protection. Since the pH is still acidic due to the anthropogenic emissions, more attention should be given to the implementation of environmental legislation, especially in non-European Union countries or in countries that joined recently.

Urban areas in developing countries are major sources of carbonaceous aerosols and air pollutants, pointing out the need for a detailed assessment of their levels and origin close to the source. A multi-instrument research campaign was performed in Delhi during December 2015–February 2016 aimed at exploring the pollution levels and the contribution of various sources to particulate matter (PM) concentrations, black carbon (BC) aerosols, and trace gases. The weak winds (< 5–6 m s −1 ) along with the shallow boundary layer favoured the formation of thick and persistent fog conditions, which along with the high BC (24.4 ± 12.2 μg m −3 ) concentrations lead to the formation of smog. Very high pollution levels were recorded during the campaign, with mean PM 10 , PM 2.5 , CO, NO, and O 3 concentrations of 245.5 ± 109.8 μg m −3 , 145.5 ± 69.5 μg m −3 , 1.7 ± 0.5 ppm, 7.9 ± 2.3 ppb, and 31.3 ± 18.4 ppb, respectively. This study focuses on examining the daily/diurnal cycles of the aerosol optical properties (extinction, scattering, absorption coefficients, single scattering albedo), as well as of PM and other pollutant concentrations, along with changes in meteorology (mixing-layer height and wind speed). In addition, the hot-spot pollution sources in the greater Delhi area were determined via bivariate plots and conditional bivariate probability function (CBPF), while the distant sources were examined via the concentration weighted trajectory (CWT) analysis. The results show that the highest aerosol absorption and scattering coefficients, PM, and trace gas concentrations are detected for weak winds (< 2 m s −1 ) with a preference for eastern directions, revealing high contribution from local sources and accumulation of pollutants within urban Delhi.

Air pollution has attracted increasing attention in recent years. Cluster analysis, scene analysis, and the potential source contribution function (PSCF), based on the backward trajectory model, were used to identify the transport pathways and potential source regions of PM2.5 and PM10 (particulate matter with an aerodynamic diameter of not more than 2.5 µm and 10 µm) in Changchun in 2018. In addition, the PSCF was slightly improved. The highest average monthly concentrations of PM2.5 and PM10 appeared in March and April, when they reached 53.9μg/m3 and 120.0 μg/m3, respectively. The main potential source regions of PM2.5 and PM10 were generally similar: western Jilin Province, northwestern Inner Mongolia, northeastern Liaoning Province, and the Yellow Sea region. The secondary potential source regions were southern Russia, central Mongolia, western Shandong Province, eastern Hebei Province, and eastern Jiangsu Province. The northwest and southwest directions were found to be the two pathways that mainly affect the air quality of Changchun City. Moreover, the northwestern pathway had a larger potential contribution source area than the southwestern pathway. The airflow in the southwest direction came from Liaoning Province, Shandong Province, and the Yellow Sea region. This mainly occurred in summer; its transmission distance was short; it had a relatively higher weight potential source contribution function (WPSCF) value; it can be regarded as a local source; and its representative pollutants were SO2 (sulfur dioxide), CO (carbon monoxide), and O3 (ozone). The northwestern pathway passed through Russia, Mongolia, and Inner Mongolia. The transmission distance of this pathway was longer; it had a relatively lower WPSCF value; it can be considered as a natural source to a certain extent; it mainly occurred in autumn and, especially, in winter; and the representative pollutants of this pathway were NO (nitric oxide), NOx (nitrogen oxide), PM2.5, and PM10.

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

Air pollution poses a serious threat to human health. The implementation of air pollution prevention and control policies has gradually reduced the level of atmospheric fine particles in Beijing. Exploring the latest characteristics of PM2.5 has become the key to further improving pollution reduction measures. In the current study, outdoor PM2.5 samples were collected in the spring and summer of Beijing, and the chemical species, oxidative potential (OP), and sources of PM2.5 were characterized. The mean PM2.5 concentration during the entire study period was 41.6 ± 30.9 μg m−3. Although the PM2.5 level in summer was lower, its OP level was significantly higher than that in spring. SO42–, NH4+, EC, NO3–, and OC correlated well with volume-normalized OP (OPv). Strong positive correlations were found between OPv and the following elements: Cu, Pb, Zn, Ni, As, Cr, Sn, Cd, Al, and Mn. Seven sources of PM2.5 were identified, including traffic, soil dust, secondary sulfate, coal and biomass burning, oil combustion, secondary nitrate, and industry. Multiple regression analysis indicated that coal and biomass combustion, industry, and traffic were the main contributors to the OPv in spring, while secondary sulfate, oil combustion, and industry played a leading role in summer. The source region analysis revealed that different pollution sources were related to specific geographic distributions. In addition to local emission reduction policies, multi-provincial cooperation is necessary to further improve Beijing's air quality and reduce the adverse health effects of PM2.5.