Long-term Spatial Distribution Research Articles

Innovative management strategies for groundwater logging in Aswan city and maximization of its benefits using modeling techniques

Groundwater levels vary from region to another and sometimes in different zones in the same country due to different boundary conditions and extraction rates. Therefore, understanding intricate aquifer systems and predicting how they will react to hydrological changes require the use of groundwater models. In Egypt, the groundwater levels in the Nile Delta aquifer decrease causing problems to the delta ecosystem while it is rising in Aswan area due to the presence of Nasser Lake causing several damages to the city’s buildings and infrastructures. In order to maximize its benefits and lessen the harm brought on by inadequate groundwater management in the city of Aswan, the height of the groundwater level in that city was examined, appraised, and groundwater management scenarios were established in this study. To achieve the objectives of the study, a simulation of Aswan aquifer’s groundwater system is built based on a quasi-three-dimensional transient groundwater flow model using MODFLOW. The model was calibrated and verified. Four management scenarios are tested. The fifth scenario, in this scenario, the four scenarios combined together at the same time and with the same conditions and ratios were proposed to be implemented. The results of the proposal to implement the four scenarios together showed that the rates of decline in groundwater levels in the last stage will be 12.44%. The study results reveal that a better understanding of the simulated long-term average spatial distribution of water balance components is useful for managing and planning the available water resources in the Aswan aquifer.

Steady-state semi-analytical solutions for assessing the two-dimensional groundwater hydraulic head depletion induced by river dam removal

Dams are pervasive features of the world’s river systems. The reservoir associated with the dam modifies the distribution of the hydraulic head in the aquifer and the natural groundwater flow. However, such modifications must be defined to forecast their environmental, economic and/or social impacts. Based on the method of fundamental solutions (MFS), steady-state semi-analytical solutions are proposed for evaluating the long-term spatial distribution of the rise or decline of the hydraulic head in an aquifer caused by a dam reservoir on a watercourse or its removal. MFS was chosen because of its ability to account for different boundary conditions along the banks of the watercourse. The dam reservoir can have a triangular or rectilinear geometry. The aquifer is assumed unconfined, homogeneous, inclined and finite. The solutions take into account both the reservoir-aquifer interaction and those between the stream and the aquifer up- and downstream of the reservoir. The reservoir-stream system fully penetrates the aquifer, and is separated from the aquifer by semi-pervious banks (Robin condition). Analysis of the semi-analytical solution included sensitivity tests, and assessment of the influence of several parameters: reservoir leakance parameter, width of the reservoir and width of the aquifer. The results show that the reservoir and river leakage parameters up- and downstream of the reservoir, as well as the geometry of the reservoir dam, are the main determining parameters of the extent of the induced groundwater mound. They show that low aquifer-river exchanges in the river upstream and downstream of the reservoir increases the spatial extension of the mound. The degree of error introduced by the method of linearization used to solve the governing partial differential equation is also discussed. A solution has been also developed with a pumping well, particularly to assess what the depletion of the water level at the well location might be if the dam is removed. The proposed solutions were applied at two field sites, giving satisfactory results.These semi-analytical solutions will be useful applications for assessing the long-term spatial impact on the aquifer of the emplacement, removal or leveling of a reservoir dam.

Long-term spatial distribution and implication of black and brown carbon in the Tibetan Plateau

Black carbon (BC) and brown carbon (BrC) over the high-altitude Tibetan Plateau (TP) can significantly influence regional and global climate change as well as glacial melting. However, obtaining plateau-scale in situ observations is challenging due to its high altitude. By integrating reanalysis data with on-site measurements, the spatial distribution of BC and BrC can be accurately estimated using the random forest algorithm (RF). In our study, the on-site observations of BC and BrC were successively conducted at four sites from 2018 to 2021. Ground-level BC and BrC concentrations were then obtained at a spatial resolution of 0.25° × 0.25° for three periods (including Periods-1980, 2000, and 2020) using RF and multi-source data. The highest annual concentrations of BC (1363.9 ± 338.7 ng/m3) and BrC (372.1 ± 96.2 ng/m3) were observed during Period-2000. BC contributed a dominant proportion of carbonaceous aerosol, with concentrations 3–4 times higher than those of BrC across the three periods. The ratios of BrC to BC decreased from Period-1980 to Period-2020, indicating the increasing importance of BC over the TP. Spatial distributions of plateau-scale BC and BrC concentrations showed heightened levels in the southeastern TP, particularly during Period-2000. These findings significantly enhance our understanding of the spatio-temporal distribution of light-absorbing carbonaceous aerosol over the TP.

Forecasting the Long-Term Spatial Distribution of Earthquakes for the 2023 U.S. National Seismic Hazard Model Using Gridded Seismicity

ABSTRACT Probabilistic seismic hazard analyses such as the U.S. National Seismic Hazard Model (NSHM) typically rely on declustering and spatially smoothing an earthquake catalog to estimate a long-term time-independent (background) seismicity rate to forecast future seismicity. In support of the U.S. Geological Survey’s (USGS) 2023 update to the NSHM, we update the methods used to develop this background or gridded seismicity model component of the NSHM. As in 2018, we use a combination of fixed and adaptive kernel Gaussian smoothing. However, we implement two additional declustering methods to account for the fact that declustering is a nonunique process. These new declustering methods result in different forecasts for the locations of future seismicity, as represented by spatial probability density functions that are later combined with a rate model to produce a full gridded earthquake rate forecast. The method updates, particularly in the separation of the spatial and rate models as well as revised regional boundaries, in some places cause substantive changes to the seismic hazard forecast compared to the previous 2018 NSHM. Additional updates to catalog processing and induced seismicity zones also contribute to changes in the gridded seismicity hazard since 2018. However, these changes are well understood and reflect improvements in our modeling of gridded seismicity hazard.

Long-term monitoring and phenological analysis of submerged aquatic vegetation in a shallow lake using time-series imagery

Submerged aquatic vegetation (SAV) is a key functional group for the restoration of wetlands and aquatic ecosystems. It plays a vital role in the stability of ecosystem structure and function in shallow lakes and reservoirs. With Baiyangdian Lake as the research area, Landsat TM/ETM+/OLI and Sentinel-2 imagery datasets were used to explore the phenological characteristics of SAV over different time scales and construct a phenological map of the SAV groups. The long-term spatial distribution characteristics and trends of SAV from 1986 to 2021 were analyzed. The results show the following: (1) the harmonic analysis of time series (HANTS) eliminated abnormal observations and noise. Normalized difference vegetation index (NDVI) time series curves generated after HANTS accurately reflected the phenological characteristics of SAV. The early spring submerged aquatic vegetation (SAV1), such as Potamogeton crispus, entered the germination period in late February at the earliest (DOY = 53). The mature period ran from April to May, after which SAV1 began to decline gradually. The length of the growing season (LOS) was about 110 days. SAV2, which was represented by Ceratophyllum demersum, Myriophyllum verticillatum, and Potamogeton pectinatus, germinated in early April (DOY = 99). The mature period ran from July to September. The length of the growing season was 210 days. (2) The decision tree constructed based on the NDVI and modified normalized difference water index (MNDWI) identified SAV with an accuracy of 89.7%. (3) The distribution range of the SAV changed dramatically during 1986–2021. According to the area change, it can be divided into a shrinkage period, expansion period, degeneration period, and initial period of recovery. The succession trend of SAV provide a theoretical scientific basis and technical support for the ecological restoration and management of shallow lake ecosystems.

Initial shape reconstruction of a volcanic island as a tool for quantifying long-term coastal erosion: the case of Corvo Island (Azores)

Abstract. Long-term coastal erosion is not yet well studied given that it is difficult to quantify. The quantification of long-term coastal erosion requires reconstruction of the coast's initial geometry and the determination of where and when the erosion started. Volcanic islands fulfill these two conditions: their initial shape is roughly conical and the age of the lavas that generated this geometry is easily measured. We have developed a method to reconstruct the initial shape of simple volcanic edifices from aerial and submarine topographic data. The reconstructed initial shape and associated uncertainties allow us to spatially quantify the coastal erosion since the building of the island. This method is applied to Corvo Island in the Azores archipelago. We calculated that, due to coastal erosion, the island has lost a volume of 6.5 ± 2.7 km3 and roughly 80 % of its surface area since it first came into being. Taking the large uncertainty in the age of the topmost lava flows (0.43 ± 0.34 Myr) into account, we have estimated that Corvo Island has lost an average of 5000 to 100 000 m3 yr−1 of its volume due to coastal erosion. Lastly, we show a strong correlation between long-term coastal erosion and the spatial distribution of the waves. Specifically, we highlight a stronger control on erosion by smaller and more frequent waves than by storm waves. The next step will be to apply this method to other volcanic islands in order to (i) streamline and improve the method and (ii) verify the correlations observed in the present study.

Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales.

Analyzing Long-Term High-Rise Building Areas Changes Using Deep Learning and Multisource Satellite Images

High-rise building areas (HRBs) provide significant social and environmental services and play a crucial role in modern urbanization. The large-scale and long-term spatial distribution of HRBs is of great interest to many fields, such as urban planning and local climate analysis. While previous studies have confirmed the value of Sentinel-2 images in extracting HRBs and their changes, current work is limited to relatively local areas and short-term analysis. One reason is due to the fact that the earliest Sentinel-2 image can only date back to 2015. To address this research gap, this paper proposes an efficient procedure to intelligently extract HRBs and their changes from multitemporal Landsat-7 and Sentinel-2 images, using a specifically designed fully convolutional network. To validate the proposed method, we selected four typical cities in China, namely, Beijing, Shanghai, Guangzhou, and Zhengzhou, as study areas. We utilized Landsat-7 images acquired in 2000 and 2010, along with Sentinel-2 images acquired in 2020, as experimental data. We extracted and analyzed three periods of HRBs and their changes in the four cities, along with urban rail terminal data and gross domestic product (GDP) data in the same period. The results show that the proposed method can efficiently extract HRBs and their changes in the four cities over the past 20 years, with an overall accuracy of more than 90%. HRBs changes are primarily driven by urban planning policies and geographical factors. There is a strong positive correlation between the increase in HRBs and the increase in rail terminals, both in terms of quantity and spatial distribution. Additionally, there is a positive correlation between HRBs increase and GDP increase in terms of quantity, but the trend varies in different cities due to their diverse developing modes. Overall, the results indicate that the proposed method can be a potential operational tool to extract large-scale and long-term HRBs and their changes in China.

Niche theory for positive plant-soil feedbacks.

Interactions between plants and the soil are an important ecological process in terrestrial ecosystems as they affect plant community structure: when and where we find different plant species. Those interactions are typically thought of as one-directional: local soil conditions filter through dispersing species to produce a community of locally adapted plants. However, plants can modify local physicochemical soil conditions via their roots and associations with soil microbes. These may in turn affect the local fitness of other plants, making plant-soil interactions bidirectional. In order to understand how they differ from other ecological processes that structure plant communities, we need a theory connecting these individual-level plant-soil feedbacks to community-level patterns. Here, we build this theory with a mathematical model of plant community dynamics in which soil conditioning is explicitly modeled over time and depends on the density of the plants. We analyze this model to describe the long-term composition and spatial distribution of the plant community. Our main result is that positive plant-soil feedbacks will create clustering of species with similar soil preferences. The composition of these clusters is further influenced by niche width and conditioning strength. In contrast with competitive dynamics driven by niche overlap, only species belonging to the same cluster can maintain high relative abundance in the community. Spatial heterogeneity in the form of an environmental gradient generates patches, each representing a single cluster. However, such patchiness is disfavored when species differ in dispersal ability. We show that stronger dispersers cannot take over the habitat as long as an exogenous driver favors soil conditions that benefit the other species. If exogenous drivers supersede soil conditioning by plants, we retrieve classic habitat filtering, where species are selected based on their suitability to the local environment. Overall, we provide a novel mathematical model for positive plant-soil feedback that we use to describe the spatial patterns of plant abundance and traits related to soil preference and conditioning ability.

A comparison of statistical and machine-learning approaches for spatiotemporal modeling of nitrogen dioxide across Switzerland

Land use regression modeling has commonly been used to model ambient air pollutant concentrations in environmental epidemiological studies. Recently, other statistical and machine-learning methods have also been applied to model air pollution, but their relative strengths and limitations have not been extensively investigated. In this study, we developed and compared land-use statistical and machine-learning models at annual, monthly and daily scales estimating ground-level NO2 concentrations across Switzerland (at high spatial resolution 100 × 100 m). Our study showed that the best model type varies with context, particularly with temporal resolution and training data size. Linear-regression-based models were useful in predicting long-term (annual, monthly) spatial distribution of NO2 and outperformed machine-learning models. However, linear-regression-based models were limited in representing short-term temporal variation even when predictor variables with temporal variability were provided. Machine-learning models showed high capability in predicting short-term temporal variation and outperformed linear-regression-based models for modeling NO2 variation at high temporal resolution (daily). However, the best performing models, XGBoost and LightGBM, constantly overfit on training data and may result in erratic patterns in the model-estimated concentration surfaces. Therefore, the temporal and spatial scale of the study is an important factor on which the choice of the suitable model type should be based and validation is required whatever approach is used.

Estimating long-term spatial distribution of Plodia interpunctella in various food facilities at Rajshahi Municipality, Bangladesh, through pheromone-baited traps.

The Indian meal moth, Plodia interpunctella (Hübner), feeds on a wide range of commodities in most regions of the world. The present study presents six years of monitoring data for P. interpunctella in pheromone-baited traps by analyzing the trends of spatial variability, in five food facilities located in the Rajshahi municipality area of Bangladesh. We also tracked insect incidence at different spatial scales and evaluated the impact of food facility types and storage structures on insect populations. Our model showed an aggregated distribution pattern for P. interpunctella. Population patterns of P. interpunctella varied significantly among facilities. The highest number of moths captured was recorded in a grain retailer located at Municipal market, followed by a grain warehouse at Harian, pulse mill at Kazla, flour mill at Sapura and grocery shop at Katakhali. The population fluctuation of P. interpunctella moths was similar among the different locations tested, while there were no captures during the winter period. Our results indicate that long-term monitoring in a wide range of areas can be used to indicate population outbursts, under an area-wide management strategy.

How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?

Strain rates have been included in multiplicative hybrid modelling of the long-term spatial distribution of earthquakes in New Zealand (NZ) since 2017. Previous modelling has shown a strain rate model to be the most informative input to explain earthquake locations over a fitting period from 1987 to 2006 and a testing period from 2012 to 2015. In the present study, three different shear strain rate models have been included separately as covariates in NZ multiplicative hybrid models, along with other covariates based on known fault locations, their associated slip rates, and proximity to the plate interface. Although the strain rate models differ in their details, there are similarities in their contributions to the performance of hybrid models in terms of information gain per earthquake (IGPE). The inclusion of each strain rate model improves the performance of hybrid models during the previously adopted fitting and testing periods. However, the hybrid models, including strain rates, perform poorly in a reverse testing period from 1951 to 1986. Molchan error diagrams show that the correlations of the strain rate models with earthquake locations are lower over the reverse testing period than from 1987 onwards. Smoothed scatter plots of the strain rate covariates associated with target earthquakes versus time confirm the relatively low correlations before 1987. Moreover, these analyses show that other covariates of the multiplicative models, such as proximity to the plate interface and proximity to mapped faults, were better correlated with earthquake locations prior to 1987. These results suggest that strain rate models based on only a few decades of available geodetic data from a limited network of GNSS stations may not be good indicators of where earthquakes occur over a long time frame.

Climatological Characteristics and Aerosol Loading Trends from 2001 to 2020 Based on MODIS MAIAC Data for Tianjin, North China Plain

The North China Plain (NCP) in East Asia has a severe air pollution problem. In this study, the long-term spatial distribution and interannual trends of aerosol optical depth (AOD) were investigated using the MODIS MAIAC (multiangle implementation of the atmospheric correction) dataset from 2001 to 2020 for Tianjin, a city on the NCP. The annual AOD in Tianjin was 0.59 from 2001 to 2020. The average AOD of Tianjin was the highest in summer (0.96), followed by spring (0.58) and autumn (0.51). The annual AOD in Tianjin increased significantly in 2008 (approximately 0.77), and the minimum annual AOD was observed in 2020 (0.41). In summer, AOD in the 11 districts of Tianjin significantly increased from 2001 to 2010 and gradually decreased from 2011 to 2020. The occurrence frequency of AOD in the range of 0.2–0.5 was high in Tianjin accounting for almost 40% of the total proportion. In Tianjin, AOD exhibited a positive trend from 2001 to 2008 and an obvious negative growth trend from 2009 to 2020 due to anthropogenic emission. The findings are valuable for analyzing the climatological characteristics of aerosol loading and their optical properties at the district level of cities on the NCP.

The long-term spatial and temporal distribution of aerosol optical depth and its associated atmospheric circulation over Southeast Africa.

Recent studies have revealed significant impact of anthropogenic aerosols to both climate and human health. Even though significant efforts have been made across the globe, studies related to aerosols over Southeast Africa remain scanty, hence causing high uncertainty in predicting and understanding the impacts of these aerosols. The present study therefore analyzed long-term spatial and temporal distribution of aerosol optical depth at 550 nanometer wavelength (AOD550) over the entire Southeast Africa. Relatively low AOD550 has been detected over the region in comparison to highly polluted regions across the globe. The highest annual average (>0.2) was observed over Lake Malawi, Zambezi valley, and the coastal areas of Central Mozambique while low annual values were recorded over northeast of Mozambique, and the eastern areas of Zimbabwe. In terms of seasonality, AOD550 was observed to be high (>0.3) during the dry months of September-October-November (SON) while being low (<0.1) during March-April-May (MAM) and June-July-August (JJA) in most areas. The seasonality of AOD550 was observed to be highly influenced by changes in seasonal biomass burning and seasonal atmospheric circulation. Statistical analyses revealed an insignificant change of AOD550 between 2002 and 2020 in most areas of the study domain. Regional open burning of biomass like bush fires and burning of crop residues during the dry months are the main sources of aerosol concentration. Therefore, the present study advocates the regulation and institutionalization of proactive and strategic mechanisms that curtail open burning activities within the region.

The Seismic Hazard Implications of Declustering and Poisson Assumptions Inferred from a Fully Time-Dependent Model

ABSTRACT We use the Third Uniform California Earthquake Rupture Forecast (UCERF3) epidemic-type aftershock sequence (ETAS) model (UCERF3-ETAS) to evaluate the effects of declustering and Poisson assumptions on seismic hazard estimates. Although declustering is necessary to infer the long-term spatial distribution of earthquake rates, the question is whether it is also necessary to honor the Poisson assumption in classic probabilistic seismic hazard assessment. We use 500,000 yr, M ≥ 2.5 synthetic catalogs to address this question, for which UCERF3-ETAS exhibits realistic spatiotemporal clustering effects (e.g., aftershocks). We find that Gardner and Knopoff (1974) declustering, used in the U.S. Geological Survey seismic hazard models, lowers 2% in 50 yr and risk-targeted ground-motion hazard metrics by about 4% on average (compared with the full time-dependent [TD] model), with the reduction being 5% at 40% in 50 yr ground motions. Keeping all earthquakes and treating them as a Poisson process increases these same hazard metrics by about 3%–12%, on average, due to the removal of relatively quiet time periods in the full TD model. In the interest of model simplification, bias minimization, and consideration of the probabilities of multiple exceedances, we agree with others (Marzocchi and Taroni, 2014) that we are better off keeping aftershocks and treating them as a Poisson process rather than removing them from hazard consideration via declustering. Honoring the true time dependence, however, will likely be important for other hazard and risk metrics, and this study further exemplifies how this can now be evaluated more extensively.

Long-Term Temporal and Spatial Distribution of Coliform Bacteria in Jiaozhou Bay Associated With Human Activities and Environmental Governance

Coliform bacteria (CB) can be used as an indicator of seawater quality. Long-term monitoring of seawater quality based on CB abundance is lacking in Jiaozhou Bay. In this study, CB abundance in surface seawater of 12 different stations in Jiaozhou Bay was investigated by culturing method. The results showed that: (1) the abundance of CB showed a decreasing tendency during the investigation. During 2004–2007, 2008–2013, and 2014–2017, the average CB abundance decreased significantly, forming a “three stages phenomenon”; (2) the average CB abundance in the first half of the year was lower than that in the second half; (3) the CB abundance in Jiaozhou Bay was spatially heterogeneous. The maximum average CB abundance was observed in the estuary area, and followed by the bay mouth area, the outer bay area, and the inner bay area. The highest abundance may be associated with sewage discharge related to human activities; (4) the abundance of CB was most positively correlated with the concentration of ammonium salt and nitrate, while most negatively correlated with salinity; (5) the years 2007 (2008) and 2013 (2014) were time points of the “three stages phenomenon.” These time points coincide with environmental governance actions, indicating that the actions have played a prominent role in improving seawater quality. Long-term survey of CB can not only serve as an indicator of seawater quality, but also provide a basis for the development of environmental governance strategies and pollution control.

Prediction of surf-zone and open-ocean airborne sea-salt spatial distribution via computational fluid dynamics and statistical method

ABSTRACT The atmospheric corrosion of steel is a critical design and maintenance issue for structures such as electric transmission towers and bridges, which are generally widely dispersed. As airborne sea-salt particles are a major corrosion factor, the long-term cumulative spatial distributions of sea salt deposited on the structure surfaces are needed. In this study, we predict the long-term cumulative spatial distribution of sea-salt particles originating from the surf zone and from the open ocean, combining a computational fluid dynamics simulation and a statistical procedure. A new prediction method is introduced for sea-salt particle concentration originating from the surf zone and is validated based on comparisons with observed data. The predicted results indicate that surf-zone-originating particles increase remarkably the amount of airborne sea-salt particles near the coastline and can travel more than 5 km inland.

Random forest model based fine scale spatiotemporal O3 trends in the Beijing-Tianjin-Hebei region in China, 2010 to 2017.

Ambient ozone (O3) concentrations have shown an upward trend in China and its health hazards have also been recognized in recent years. High-resolution exposure data based on statistical models are needed. Our study aimed to build high-performance random forest (RF) models based on training data from 2013 to 2017 in the Beijing-Tianjin-Hebei (BTH) region in China at a 0.01°×0.01° resolution, and estimated daily maximum 8h average O3 (O3-8hmax) concentration, daily average O3 (O3-mean) concentration, and daily maximum 1h O3 (O3-1hmax) concentration from 2010 to 2017. Model features included meteorological variables, chemical transport model output variables, geographic variables, and population data. The test-R2 of sample-based O3-8hmax, O3-mean and O3-1hmax models were all greater than 0.80, while the R2 of site-based and date-based model were 0.68-0.87. From 2010 to 2017, O3-8hmax, O3-mean, and O3-1hmax concentrations in the BTH region increased by 4.18μg/m3, 0.11μg/m3, and 4.71μg/m3, especially in more developed regions. Due to the influence of weather conditions, which showed high contribution to the model, the long-term spatial distribution of O3 concentrations indicated a similar pattern as altitude, where high concentration levels were distributed in regions with higher altitude.

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study.

In studies showing associations between ambient air pollution and myocardial infarction (MI), data have been lacking on the inherent spatial variability of air pollution. The aim of this study was to determine whether the long-term spatial distribution of air pollution influences short-term temporal associations between air pollution and admission to hospital for MI. We identified adults living in Calgary who were admitted to hospital for an MI between 2004 and 2012. We evaluated associations between short-term exposure to air pollution (ozone [O3], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], particulate matter < 10 μm in diameter [PM10] and particulate matter < 2.5 μm in diameter [PM2.5]), and hospital admissions for MI using a time-stratified, case-crossover study design. Air Quality Health Index (AQHI) scores were calculated from a composition of O3, NO2 and PM2.5. Conditional logistic regression models were stratified by low, medium and high levels of neighbourhood NO2 concentrations derived from land use regression models; results of these analyses are presented as odds ratios (ORs) with 95% confidence intervals (CIs). From 2004 to 2012, 6142 MIs were recorded in Calgary. Individuals living in neighbourhoods with higher long-term air pollution concentrations were more likely to be admitted to hospital for MI after short-term elevations in air pollution (e.g., 5-day average NO2: OR 1.20, 95% CI 1.03-1.40, per interquartile range [IQR]) as compared with regions with lower air pollution (e.g., 5-day average NO2: OR 0.90, 95% CI 0.78-1.04, per IQR). In high NO2 tertiles, the AQHI score was associated with MI (e.g., 5-day average OR 1.13, 95% CI 1.02-1.24, per IQR; 3-day average OR 1.13, 95% CI 1.04-1.23, per IQR). Our results show that the effect of air pollution on hospital admissions for MI was stronger in areas with higher NO2 concentrations than that in areas with lower NO2 concentrations. Individuals living in neighbourhoods with higher traffic-related pollution should be advised of the health risks and be attentive to special air quality warnings.

Analysis of water clarity decrease in Xin’anjiang Reservoir, China, from 30-Year Landsat TM, ETM+, and OLI observations

Water clarity controls a wide range of physical, chemical, and biological processes and thus is an important environmental variable for assessing both the climatic and human activity factors that affect lake ecosystems. Here, we use 30-year Landsat series data to investigate the patterns and trends of water clarity in the Xin’anjiang Reservoir, the largest freshwater, man-made reservoir in the Yangtze River Delta region of China. Three empirical models based on Landsat 5, 7 and 8 data were developed and validated to remotely estimate the water clarity. The validation results show that the three models performed well in deriving water clarity, with the mean absolute percent differences (MAPDs) <30%. The long-term trends and spatial distribution patterns of water clarity from 1986 to 2016 were constructed using the three developed models. A significant decreasing trend in water clarity was observed, with a reduction of approximately 0.2 m, indicating a gradual water quality decrease. Eleven explanatory variables, including air temperature, rainfall, gross domestic product (GDP), population, chemical fertilizer usage, industrial sewage, and land-use change, were used to explore the driving force of the long-term trend of water clarity with the multiple regression and variation partitioning model. Air temperature, rainfall, cropland, and industrial sewage were the four potential driving forces and explained 63% of the long-term variation in water clarity. Increasing built-up land, air temperature and rainfall, and decreasing forest and grassland jointly caused the decreasing water clarity. Therefore, several reservoir management measurements should be implemented to stop water clarity decreasing.