Fine-scale Spatial Distribution Research Articles

Predictable oceanographic processes drive the formation of a distribution hotspot for Indo-Pacific humpback dolphins at a manmade harbor

Marine mammals often aggregate and forage in specific ‘hotspot’ areas, which are associated with the predictability of prey sources driven by certain oceanographic processes. However, our understanding of the interactions between marine predators and the oceanographic processes remains incomplete, particularly in near-coastal and estuarine regions. This study investigated how predictable oceanographic processes influence the distribution patterns of Indo-Pacific humpback dolphins (Sousa chinensis) foraging in a known hotspot within the Pearl River Estuary, characterized by the complex topography of its manmade harbor and the interplay between strong tidal flows and river plumes. To address the fine-scale spatial distribution pattern of dolphin foraging, we conducted fixed passive acoustic monitoring of foraging ‘buzzes’ both inside and outside the harbor. Additionally, boat-based transect surveys were conducted to explore the relationship between dolphin foraging and associated water properties and current fields driven by oceanographic processes. Temporal trend analyses of buzzes revealed a pronounced tidal-related pattern in dolphin foraging activity, with increased detections during ebbing tides outside the harbor. Transect surveys indicated areas with elevated chlorophyll and turbidity levels attracted more dolphins during flood tides, whereas regions with high surface salinity gradients were preferred by dolphins during ebb tides. Oceanographic analysis recognized these bio-physical links as indicators of oceanographic features, including chlorophyll-rich river plumes, converging fronts, and shear-induced turbulence. These features enhance prey abundance and availability for humpback dolphins. This study highlights that oceanographic processes are crucial for determining the fine-scale spatiotemporal distribution of foraging in coastal dolphins, and offers insights into the effective prediction of dolphin aggregation.

Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners

Statistical stream temperature models predicting the fine-scale spatial distribution of water temperatures (i.e., “thermalscape”) can guide aquatic species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence of sampling sites within dendritic networks. We used August mean temperature data from miniature sensors deployed in Canadian Rocky Mountain streams to demonstrate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was a spatial steam network (SSN) model specifically developed to account for spatial autocorrelation in dendritic stream networks. The second was an integrated nested Laplace approximation (INLA) model that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitting SSN and INLA models using leave-one-out cross-validation. Relative to INLA, SSN models had lower RMSE (1.23 vs. 1.45 C) and higher r2 (0.71 vs. 0.61); however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice, an open-access r-script, and data to help non-experts develop statistical stream temperature models.

Fine-scale spatial distribution of fish community in high-rise artificial reefs investigated using an underwater drone and quantitative environmental DNA metabarcoding

Fine-scale spatial distribution of fish community in high-rise artificial reefs investigated using an underwater drone and quantitative environmental DNA metabarcoding

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision.

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.

Spawning aggregations of checkered snapper (Lutjanus decussatus) and blackspot snapper (L. fulviflamma): seasonality, lunar-phase periodicity and spatial distribution within spawning ground.

Snappers (family Lutjanidae) are important fisheries target species and some species are known to form spawning aggregations at particular spawning grounds. The present study investigated the ecological characteristics of fish aggregations of two snapper species (checkered snapper Lutjanus decussatus and blackspot snapper L. fulviflamma) that form at a particular site. Specifically, the aims were to clarify (1) seasonality and lunar-phase periodicity of fish aggregation formation, (2) fine-scale spatial distribution of fish density (spatial variations of fish density at intervals of several-tens meters) within the aggregation site, (3) size and age frequency distributions of fishes in the aggregation site, (4) gonad development, (5) to compare fish abundance between inside and outside the aggregation site, and (6) to verify that fish aggregations of the two snapper species were spawning aggregation. Underwater observations using a 600m×5m transect revealed that greater fish abundance of Lutjanus decussatus was found monthly between May and October, and clear positive peaks in the fish abundance were found only around the last-quarter moon. This lunar-related periodicity in the increase of fish abundance was confirmed by a time-series analysis (correlogram). Within the aggregation site, L. decussatus showed a relatively uniform distribution. In contrast, greater fish abundance of L. fulviflamma was found monthly between April and October, and clear positive peaks in the fish abundance were found around the last-quarter moon (April, May, June and October) or new moon (July, August and September). This lunar-related periodicity was also confirmed by correlogram. Lutjanus fulviflamma showed a relatively clumped distribution within the aggregation site. Most females of the two species in the aggregation site had hydrated eggs, indicating that the two species form aggregations for reproduction. The two species, although occurring simultaneously, are considered to form aggregations of conspecifics only. For L. decussatus, average fork length and age of males and females were 229.2 mm and 243.9 mm and 9.4 years and 8.1 years, respectively. For L. fulviflamma, average fork length and age of males and females were 233.9 mm and 246.9 mm and 6.8 years and 8.1 years, respectively. Fish abundance inside the aggregation site was 266.8-fold and 141557.1-fold greater than those outside the aggregation site for L. decussatus and L. fulviflamma, respectively. These results showed that (1) fish aggregation formation of the two snapper species was predictably repeated in particular months and lunar-phase, (2) it was predictably found at the particular site, (3) the fish abundance in the aggregation site markedly exceeded the fish abundance outside the aggregation site, and (4) the two species form aggregations for reproduction. Therefore, it is suggested that the fish aggregations for the two species can be regarded as spawning aggregations.

Major fine-scale spatial heterogeneity in accumulation of gelatinous carbon fluxes on the deep seabed

Abyssal plain communities rely on the overlying water column for a settling flux of organic matter. The origin and rate of this flux as well as the controls on its fine-scale spatial distribution following seafloor settlement are largely unquantified. This is particularly true across regions where anthropogenically-induced seafloor disturbance has occurred. Here, we observed, quantified and mapped a mass deposition event of gelatinous zooplankton carcasses (pyrosomes) in July-September 2015 across one such physically disturbed region in the Peru Basin polymetallic nodule province (4150 m). Seafloor in this area was disturbed with a plough harrow in 1989 (as part of the DISCOL experiment) causing troughs in the sediment. Other parts were disturbed with an epibenthic sled (EBS) during a cruise in 2015 resulting in steep-walled, U-shaped troughs. We investigated two hypotheses: a) gelatinous food falls contribute significantly to the abyssal plain carbon pump and b) physical seafloor disturbance influences abyssal distribution of organic matter. We combined optical and bathymetric seafloor observations, to analyze pyrosome distribution on seabeds with different levels of disturbance. 2954 pyrosome colonies and associated taxa were detected in > 14,000 seafloor images. The mean regional carbon (C) deposition associated with pyrosome carcasses was significant compared to the flux of particulate organic C (182 to 1543%), and the total respired benthic C flux in the DISCOL Experimental Area (39 to 184%). EBS-disturbed seafloor tracks contained 72 times more pyrosome-associated C than an undisturbed reference site, and up to 4 times more than an area disturbed in 1989. Deposited pyrosomes collected had a higher proportion of labile fatty acids compared to the sediment. We document the temporal and spatial extent of an abyssal food fall event with unprecedented detail and show that physical seafloor disturbance results in the accumulation of detrital material. Such accumulation may reduce oxygen availability and alter benthic community structure. Understanding both the relevance of large food falls and the fine scale topography of the seafloor, is necessary for impact assessment of technologies altering seafloor integrity (e.g. as a result of bottom-trawling or deep seabed mining) and may improve their management on a global scale.

Fine-scale spatial distribution of deltamethrin resistance and population structure of Anopheles funestus and Anopheles arabiensis populations in Southern Mozambique

BackgroundInsecticide resistance in malaria vectors can be spatially highly heterogeneous, yet population structure analyses frequently find relatively high levels of gene flow among mosquito populations. Few studies have contemporaneously assessed phenotypic, genotypic and population structure analysis on mosquito populations and none at fine geographical scales. In this study, genetic diversity, population structure, and insecticide resistance profiles of Anopheles funestus and Anopheles arabiensis were examined across mosquito populations from and within neighbouring villages.MethodsMosquitoes were collected from 11 towns in southern Mozambique, as well as from different neighbourhoods within the town of Palmeira, during the peak malaria transmission season in 2016. CDC bottle bioassay and PCR assays were performed with Anopheles mosquitoes at each site to determine phenotypic and molecular insecticide resistance profiles, respectively. Microsatellite analysis was conducted on a subsample of mosquitoes to estimate genetic diversity and population structure.ResultsPhenotypic insecticide resistance to deltamethrin was observed in An. funestus sensu stricto (s.s.) throughout the area, though a high level of mortality variation was seen. However, 98% of An. funestus s.s. were CYP6P9a homozygous resistant. An. arabiensis was phenotypically susceptible to deltamethrin and 99% were kdr homozygous susceptible. Both Anopheles species exhibited high allelic richness and heterozygosity. Significant deviations from Hardy–Weinberg equilibrium were observed, and high linkage disequilibrium was seen for An. funestus s.s., supporting population subdivision. However, the FST values were low for both anophelines (− 0.00457 to 0.04213), Nm values were high (9.4–71.8 migrants per generation), AMOVA results showed almost 100% genetic variation among and within individuals, and Structure analysis showed no clustering of An. funestus s.s. and An. arabiensis populations. These results suggest high gene flow among mosquito populations.ConclusionDespite a relatively high level of phenotypic variation in the An. funestus population, molecular analysis shows the population is admixed. These data indicate that CYP6P9a resistance markers do not capture all phenotypic variation in the area, but also that resistance genes of high impact are likely to easily spread in the area. Conversely, other strategies, such as transgenic mosquito release programmes will likely not face challenges in this locality.

Mycorrhizal diversity and community composition in co-occurring Cypripedium species.

Orchids commonly rely on mycorrhizal fungi to obtain the necessary resources for seed germination and growth. Whereas most photosynthetic orchids typically associate with so-called rhizoctonia fungi to complete their life cycle, there is increasing evidence that other fungi may be involved as well and that the mycorrhizal communities associated with orchids may be more diverse. Coexisting orchid species also tend to associate with different fungi to reduce competition for similar resources and to increase long-term population viability. However, few studies have related the mycorrhizal communities in the rhizosphere to communities found in the roots of closely related coexisting orchid species. In this study, we used high-throughput sequencing to investigate the diversity and community composition of orchid mycorrhizal fungi in the roots and the rhizosphere of four Cypripedium species growing in forests in Northeast China. The results showed that the investigated Cypripedium species associated with a wide variety of fungi including members of Tulasnellaceae, Psathyrellaceae, and Herpotrichiellaceae, whereas members of Russulaceae, Cortinariaceae, Thelephoraceae, and Herpotrichiellaceae showed high abundance in rhizosphere soils. The diversity of fungi detected in the rhizosphere soil was much higher than that in the roots. The observed variation in fungal communities in Cypripedium roots was not related to forest site or orchid species. On the other hand, variation in mycorrhizal communities of rhizosphere soil was significantly related to sampling site. These results indicate that orchid mycorrhizal communities in the rhizosphere display considerable variation among sites and that orchids use only a subset of the locally available fungi. Future studies focusing on the fine-scale spatial distribution of orchid mycorrhizal fungi and more detailed assessments of local environmental conditions will provide novel insights into the mechanisms explaining variation of fungal communities in both orchid roots and the rhizosphere.

Spatial Integration of Urban Runoff Modeling, Heat, and Social Vulnerability for Blue-Green Infrastructure Planning and Management

Climate change exacerbates environmental challenges caused by urban growth. This study offers a multifaceted approach to understanding socioenvironmental vulnerability in cities by considering fine-scale spatial distribution of social and physical metrics overlaid with runoff and surface temperature (ST) data. Stormwater runoff depth and surface temperature data for micro-subbasins in Philadelphia were estimated and integrated to create an environmental vulnerability index. Social variables representing poverty, the elderly population, population density, and buildings with basements were used to develop a social vulnerability index. Simple analysis based on medians of combined environmental and social vulnerability indexes identified specific subbasins of the city that are most vulnerable to heat and flooding impacts of climate change. Hotspot analysis of combined social and environmental vulnerability data identified regions of the city that would benefit most from a focus on implementation of new green-blue infrastructures. Results indicated that neighborhoods in southern Philadelphia are most vulnerable to flooding and heat. The outlined approach can be used for any city, and provides city managers with a city-scale visualization for prioritizing areas in immediate need of climate mitigation practices.

Linking rattiness, geography and environmental degradation to spillover Leptospira infections in marginalised urban settings: An eco-epidemiological community-based cohort study in Brazil.

Zoonotic spillover from animal reservoirs is responsible for a significant global public health burden, but the processes that promote spillover events are poorly understood in complex urban settings. Endemic transmission of Leptospira, the agent of leptospirosis, in marginalised urban communities occurs through human exposure to an environment contaminated by bacteria shed in the urine of the rat reservoir. However, it is unclear to what extent transmission is driven by variation in the distribution of rats or by the dispersal of bacteria in rainwater runoff and overflow from open sewer systems. We conducted an eco-epidemiological study in a high-risk community in Salvador, Brazil, by prospectively following a cohort of 1401 residents to ascertain serological evidence for leptospiral infections. A concurrent rat ecology study was used to collect information on the fine-scale spatial distribution of 'rattiness', our proxy for rat abundance and exposure of interest. We developed and applied a novel geostatistical framework for joint spatial modelling of multiple indices of disease reservoir abundance and human infection risk. The estimated infection rate was 51.4 (95%CI 40.4, 64.2) infections per 1000 follow-up events. Infection risk increased with age until 30 years of age and was associated with male gender. Rattiness was positively associated with infection risk for residents across the entire study area, but this effect was stronger in higher elevation areas (OR 3.27 95% CI 1.68, 19.07) than in lower elevation areas (OR 1.14 95% CI 1.05, 1.53). These findings suggest that, while frequent flooding events may disperse bacteria in regions of low elevation, environmental risk in higher elevation areas is more localised and directly driven by the distribution of local rat populations. The modelling framework developed may have broad applications in delineating complex animal-environment-human interactions during zoonotic spillover and identifying opportunities for public health intervention. This work was supported by the Oswaldo Cruz Foundation and Secretariat of Health Surveillance, Brazilian Ministry of Health, the National Institutes of Health of the United States (grant numbers F31 AI114245, R01 AI052473, U01 AI088752, R01 TW009504 and R25 TW009338); the Wellcome Trust (102330/Z/13/Z), and by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB/JCB0020/2016). MTE was supported by a Medical Research UK doctorate studentship. FBS participated in this study under a FAPESB doctorate scholarship.

Estimation of exposure and premature mortality from near-roadway fine particulate matter concentrations emitted by heavy-duty diesel trucks in Beijing

Traffic exhaust is a main source of fine particulate matter (PM2.5) in cities. Heavy-duty diesel trucks (HDDTs), the primary mode of freight transport, contribute significantly to PM2.5, posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and the public health effects of such emissions in different populations have not been thoroughly assessed. To fill this gap, we focused on Beijing as the research area and developed a high-resolution PM2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution of diesel-related PM2.5 and assessed the population exposure by integrating the dispersion model and population distributions. Further, we quantified the mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to PM2.5 emissions from HDDTs. Results showed that 3.3% of Beijing people lived in areas with high PM2.5 HDDT emissions, which were near intercity highways. Furthermore, the estimated number of NCD + LRI annual premature deaths attributed to PM2.5 HDDT emissions in Beijing was 339 (95% CI: 276–401). The NCD + LRI mortality increased with age, and deaths were more frequent in males than females. Our results aid the identification of HDDT PM2.5 emission exposure hotspots for the formulation of effective mitigation measures and provide important insights into the adverse health impacts of HDDT emissions.

Carbon emission accounting and spatial distribution of industrial entities in Beijing—Combining nighttime light data and urban functional areas

Quantifying current carbon emissions their fine scale spatial distribution is necessary to improve carbon emission management, requirements, and emission reduction strategies of key industries. This study established an entity-level model to estimate carbon emissions by combining geographic information of points of interest (POIs) and nighttime light data from Beijing in 2018. The model accounted for the carbon emissions of Beijing's key entities and industries and simulated their spatial distribution. The results showed a good fit between the carbon emissions of the entities and nighttime light brightness values. The 130-m resolution of the urban carbon emission distribution data had a higher spatial simulation accuracy than that of the 1-km Open-Data inventory for anthropogenic carbon dioxide (ODIAC) data. Through the lens of urban functional areas, the average value of carbon emissions was highest in commercial areas and lowest in public management and service areas, at 78,840.11 tC/km2 and 6844.79 tC/km2, respectively. In terms of the industrial sector, the transportation industry had the highest carbon emissions, with a total of 31.86 Mt., while non-metal mining and oil and gas extraction had almost no energy consumption, with total carbon emissions of 1.38 Mt. The spatial clustering results showed that the distribution of carbon emissions in Beijing had a significant positive spatial correlation; forming high-high aggregation clusters dominated by the city center and major business districts and a low-low aggregation clusters dominated by the city's suburban areas. The simulation model clearly reflected the fine scale characteristics of carbon emissions, in terms of their quantity and spatial distribution. Results obtained in this study can aid relevant departments to formulate appropriate strategies for collectively guiding industrial enterprises towards carbon neutrality.

Spatial pattern of functional genes abundance reveals the importance of PhoD gene harboring bacterial community for maintaining plant growth in the tropical forest of Southwestern China

The concept of microbial functional genes has added a new dimension to microbial ecology research by improving the model of microbial community-ecosystem functions relationship. However, our knowledge vis-à-vis fine-scale spatial distribution pattern of functional genes and their probable impact on plant community in the hyper-diverse tropical forest ecosystem is very limited. Here, we investigated the spatial pattern of functional genes abundance (NirK, AOA, AOB, and PhoD), identified key influencing factors, and distinguished the key functional group supporting the plant community in a tropical rainforest located in Xishuangbanna. In total, 200 soil samples and vegetation data of ~4800 individuals of plants across a 1 ha study area were collected. Our results detected higher spatial variability with a maximum magnitude of abundance for PhoD gene (4.53 × 107 copies) followed by NirK (2.71 × 106 copies), AOA (1.97 × 106 copies), and AOB (7.38 × 104 copies). A strong spatial dependence was observed for PhoD and NirK over the distance of 17 and 18 m, respectively. Interestingly, the N:P stoichiometry played a critical role in structuring the spatial pattern of the most abundant PhoD gene. The significant positive and negative relationship of PhoD with N:P ratio and available phosphorus, respectively, indicated that the P-limiting environment was a driving factor for recruitment of PhoD gene community. The structural equation modeling ascertained the direct positive impact of PhoD on plant biomass and high demand of available P by plants suggesting that the organic phosphorus mineralization process is essential to maintain plant productivity by re-establishing the availability of the most limiting P nutrient. Our preliminary study improves our understanding of how microbial functional genes-environment associations could be used for monitoring soil health and its overall impact on ecosystem multifunctionality. Finally, we intend to conduct the study at a large spatial scale for achieving a holistic view.

Comparing distribution of harbour porpoise using generalized additive models and hierarchical Bayesian models with integrated nested laplace approximation

Species Distribution Models (SDMs) are used regularly to develop management strategies, but many modelling methods ignore the spatial nature of data. To address this, we compared fine-scale spatial distribution predictions of harbour porpoise (Phocoena phocoena) using empirical aerial-video-survey data collected along the east coast of Scotland in August and September 2010 and 2014. Incorporating environmental covariates that cover habitat preferences and prey proxies, we used a traditional (and commonly implemented) Generalized Additive Model (GAM), and two Hierarchical Bayesian Modelling (HBM) approaches using Integrated Nested Laplace Approximation (INLA) model-fitting methodology. One HBM-INLA modelled gridded space (similar to the GAM), and the other dealt more explicitly in continuous space using a Log-Gaussian Cox Process (LGCP).Overall, predicted distributions in the three models were similar; however, HBMs had twice the level of certainty, showed much finer-scale patterns in porpoise distribution, and identified some areas of high relative density that were not apparent in the GAM. Spatial differences were due to how the two methods accounted for autocorrelation, spatial clustering of animals, and differences between modelling in discrete vs. continuous space; consequently, methods for spatial analyses likely depend on scale at which results, and certainty, are needed.For large-scale analysis (>5–10 km resolution, e.g. initial impact assessment), there was little difference between results; however, insights into fine-scale (<1 km) distribution of porpoise from the HBM model using LGCP, while more computationally costly, offered potential benefits for refining conservation management or mitigation measures within offshore developments or protected areas.

Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models

AbstractData-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g., overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward's hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources.

Health Effects of Near-Roadway Diesel-Related Fine Particulate Matter Concentrations in Beijing, China

Traffic exhaust is a main source of fine particulate matter (PM2.5) in cities. Heavy-duty diesel trucks (HDDTs), the main mode of freight transport, contribute significantly to PM2.5, posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and health effects in different populations have not been thoroughly assessed. To fill this gap, we, taking Beijing as the research area, constructed a high-resolution PM2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution patterns of PM2.5 by integrating the RLINE dispersion model to assess the exposure risk of different populations and to quantify mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to high PM2.5 concentrations. The results showed that the per-capita exposure dose of PM2.5 from HDDTs in Beijing first increased and then decreased with age, peaking at four times the minimum in individuals 20–44 years of age. In general, the per-capita exposure dose for male (186 μg) was significantly higher than that for female (147 μg). Moreover, the number of NCD+LRI annual premature deaths caused by PM2.5 from HDDTs in Beijing was 559 (95% confidence interval: 466–648). The number of premature deaths in male was 1.6 times higher than that in female, although the total population of male was approximately the same as that of female. Furthermore, 94.5% of premature deaths occurred in individuals 60 years age and older. These findings provide important insights into the adverse health effects from HDDTs and help authorities establish early warning strategies for high-risk populations in Beijing.

Effects of Artemisia ordosica on fine-scale spatial distribution of soil C, N and P and physical–chemical properties in the Mu Us Desert, China

Effects of Artemisia ordosica on fine-scale spatial distribution of soil C, N and P and physical–chemical properties in the Mu Us Desert, China

Historic changes of polychlorinated biphenyls (PCBs) in juvenile and adult cetaceans from the Pearl River estuary from 2003 to 2020

Polychlorinated biphenyls (PCBs), as a type of legacy persistent organic pollutants, pose significant health threats to wildlife. However, long-term residue changes and profiles of PCBs in cetaceans have not been extensively studied in the Pearl River Estuary (PRE), an important marine mammal area in China. Here, the body burdens, spatiotemporal trends, and health risks of 21 chlorobiphenyl congeners (∑21CBs) were analyzed in blubber samples collected from twelve cetacean species (n = 172) in the PRE from 2003 to 2020. Our results revealed medium levels of PCBs (316–96,233 ng g−1 lipid) compared to those reported for cetaceans elsewhere (70–370,000 ng g−1 lipid). Clear differences in PCB distribution patterns between inshore and offshore cetaceans and between odontocetes and mysticetes were also found. Both the coastal Indo-Pacific humpback dolphins (Sousa chinensis) and Indo-Pacific finless porpoises (Neophocaena phocaenoides) displayed similarly fine-scale spatial distribution patterns of PCBs, suggesting that the two cetaceans could serve as bioindicators of PCB pollution in the PRE. Additionally, both cetaceans exhibited decreasing trends in their blubber PCB concentrations over the past 20 years, likely reflecting the effective regulation of PCBs in the PRE Delta. Nevertheless, the relatively high and stable PCB-toxic equivalent (TEQ) levels detected in calf humpback dolphins during the sampling period suggested that the calves are still under the stresses of high PCB-related health risks. Our results highlight the need for more efforts to eliminate PCB contamination to prevent these cetaceans from continuous population decline and further extinction.

Fine-scale spatial distribution of soil organic carbon and its fractions after afforestation withPinus sylvestrisandSalix psammophilain a semiarid desert of China

AbstractAfforestation is helpful to improve soil functions and increase soil organic carbon (SOC) sequestration in semiarid deserts. However, the fine-scale (around a single plant) spatial distribution of SOC and its liable organic carbon (LOC) fractions after afforestation in semiarid deserts are poorly understood. Pinus sylvestris and Salix psammophila afforested on shifting sandy land (Sland) were selected to quantify fine-scale (at 20, 80, 150 and 240 cm away from the trees) spatial distribution of SOC and its LOC fractions in the southeast edge of Mu Us Desert, China. The results showed that the afforested S. psammophila and P. sylvestris significantly increased SOC, total nitrogen, dissolved organic carbon, microbial biomass carbon and readily oxidized organic carbon (ROOC). At 20 cm distance, SOC storage of P. sylvestris was 27.21% higher than S. psammophila in 0–100 cm soil layers, and SOC storage of S. psammophila at 80 and 150 cm distances was 5.50% and 5.66% higher than P. sylvestris, respectively. Compared with Sland, SOC storage under S. psammophila and P. sylvestris significantly increased by 94.90%, 39.50%, 27.10% and 18.50% at 20, 80, 150 and 240 cm distance, respectively. ROOC accounted for 14.09% and 18.93% of SOC under S. psammophila and P. sylvestris, respectively. Our results suggest that afforestation can promote SOC accumulation at different distances from the plants, and that P. sylvestris allocates more organic matter to the closer soil compared with S. psammophila (&amp;lt;80 cm from the tree).

Using a distributed air sensor network to investigate the spatiotemporal patterns of PM2.5 concentrations

Spatiotemporal variations in PM2.5 are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM2.5 in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM2.5. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM2.5 using the time-series clustering method. The results demonstrated that there were four stages of PM2.5 daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM2.5 accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM2.5 revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM2.5, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM2.5, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.