Seasonal Disparity Research Articles

Analytical estimates of short duration mean power output and variability for deepwater wave power generation

With deepwater wave power generation being seriously considered as a viable option across the marine vicinity of India, seasonal disparity of mean power as well as short duration power variability are of interest in the context of site and technology selection. Past experience with an onshore wave power installation in the region has not been altogether encouraging, which justifies good concern on the two aspects.This paper formulates closed form analytical expressions for short duration mean power generation and associated variability that may be conveniently employed for prior estimates of the two measures with reference to prospective installation sites and wave energy converter (WEC) technologies. The formulae evolved can serve as useful support at the stage of project planning and resource assessment, as well as for general viability analysis of marine power generation.As part of the exercise, the concept of capture width function is introduced for feasible operating points of a WEC technology, inclusive of functional morphing and statistical morphing effects on output power. Illustrative examples cover eight deepwater grids spread across the marine neighborhood of India, and two candidate WEC technologies considered for operational viability.

Vegetation greening in more than 94% of the Yellow River Basin (YRB) region in China during the 21st century caused jointly by warming and anthropogenic activities

Reliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activities. However, whether there has been a significant change in the intensity of vegetation dynamics in the YRB during the 21st century and how much anthropogenic disturbances contribute to the change are still poorly documented so far; thus, this work may hold a far-reaching significance for maintaining the eco-balance of northern China and even all of China. In this study, based on the annually and seasonally averaged normalized deviation vegetation index (NDVI), a comprehensive analysis was conducted to detect the trends of vegetation changes derived by NDVI at six different time scales during the period 2000–2018 using Theil-Sen statistics, and the Mann-Kendall (M-K) method was employed to test the significance levels of vegetation greening and browning. Subsequently, based on Moderate Resolution Imaging Spectroradiometer land cover data, in situ climatic and meteorological data and socioeconomic and anthropogenic factors were included to explore the driving forces for vegetation change through the GeoDetector method. Our results show that the YRB experienced both significant vegetation greening and browning with great spatial heterogeneity. More than 94% of the regions in the YRB showed vegetation greening trends at average rates of 0.0040 yr−1 in the annually averaged NDVI (NDVIAN) and 0.0053 yr−1 in the growing season averaged NDVI (NDVIGS) during the 21st century; the trend was significant in more than 71% (p < 0.05) of the total YRB territory. This phenomenon was mainly detected in the middle reaches of the YRB, where effective land use activities and afforestation played vital roles. There was great seasonal disparity for greening; spring NDVI (NDVISP) and summer NDVI (NDVISU) exhibited the largest greening rates. However, only small parts of the YRB (0.9%, p < 0.05) showed browning trends at average rates of 0.0015 yr−1 in NDVIAN and 0.0021 yr−1 in NDVIGS, mainly located in the Weihe River basin and the lower reaches of the YRB, where the sharp increase in urban and built-up areas contributed substantially to the browning trends. Overall, warming, increasing population and afforestation jointly dominated the significant vegetation greening trends in the YRB.

Damage Assessment and Spatio-Temporal Mapping Using Object Based Image Analysis Technique on Floods-2015 in Southern Punjab, Pakistan

Floods are the natural disasters which not only harm human lives but also damage entire structure in the inundated area. Pakistan has been witnessing extensive floods since very long because of long and severe spells of monsoon rainfalls with abrupt diurnal and seasonal disparity in the temperature level every year. Floods of 2010 and 2014 were so severe that they had broken the disaster record of earthquake-2005 in Pakistan and floods of 2015 had devastated the southern Punjab badly. These floods have damaged socioeconomic activities and people were displaced from the flood prone areas. Southern Punjab has been facing this calamity frequently from 2010 onward which resulting to huge destruction and gigantic loss of human lives every year. An immaculate large area of the Southern Punjab was damaged with crop land, built-up areas and road networks. To assess the damages and spatio-temporal mapping during flood period, remote sensing and GIS techniques have been incorporated by using spatial statistical and object-based image classification techniques. These techniques have been applied for estimating damages, mapping and extracting flood extents by embedding NDVI, NDBI and NDWI methods into object-based image analysis. This paper concludes assessment of damages, various extents of flood and accuracy assessment (79.3% and Khat0.72). Outcomes of the paper would be very beneficial for disaster management authorities for mitigating future floods and helpful for flood monitoring departments to guide competent management authorities at all tiers for quick response and rehabilitation programmes.

Effects of natural and anthropogenic factors and their interactions on dust events in Northern China

Aeolian dust can influence the climate, air quality, human health, and ecosystems. Dust events in Northern China are the main contributors to dust aerosols in the world, but the impacts of meteorological and anthropogenic factors and their interactions on dust events remain unclear. This study analyzed the spatial and temporal variations of dust event frequencies and quantitatively investigated the impacts of meteorological conditions, anthropogenic factors, and their interactions on dust events using the geographical detector model (GeoDetector) in Northern China. Results revealed that the dust event frequency significantly decreased by 0.006 times yr−1 per site during 1980–2007. At the regional scale, there were large seasonal variations in the effects of meteorological conditions and anthropogenic factors on dust events. Strong winds and soil surface conditions are main drivers of dust events in spring. In summer and autumn, anthropogenic factors have significant impacts on the occurrence of dust events, but the frozen period and relative humidity are major impacting factors in winter. Effects of natural and anthropogenic factors on dust events showed great spatial and seasonal disparities over different vegetation regions. Interactions between two factors enhanced their impacts on the occurrence of dust events. There are also large spatial and seasonal variations in the primary interactions on dust events over different vegetation regions. The findings could help us to better understand the relative importance of various factors on dust events, which has important implications for improving the prediction of dust emission models and developing desertification control strategies.

Role of environmental factors on nitrogen fixation rate in a tropical estuary (southwest coast of India)

Nitrogen fixation is one of the key biogeochemical processes that regulate the availability of biologically active nitrogen to the natural environments; however, ecological control of this process is still unknown. This study reports the variations of N2 fixation rate in the water and sediment environments of an estuary (southwest coast, India) as a function of environmental factors controlling this process. The N2 fixation rate was measured in-situ in the surface water and sediments by acetylene reduction assay. The N2 fixation rate was relatively high in surface water (1.07 ± 0.54 nmol N L−1 h−1) than in the sediment environment (0.27 ± 0.13 nmol N g−1 h−1) and showed spatial variation. A significant seasonal disparity in the fixation rate was elucidated in water and sediment environments (P<0.01). Further, the results demonstrate that, rate of N2 fixation correlated positively with pH (p<0.05), dissolved inorganic phosphate (p<0.05) and temperature (p<0.01), while negatively correlated with dissolved oxygen (p<0.01), dissolved inorganic nitrogen (p<0.05) and nitrogen: phosphorous ratio (p<0.05). These data emphasizes the significance of environmental factors affecting on N2 fixation process on anthropogenically disturbed aquatic ecosystem.

Top-Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest.

Agriculture and waste are thought to account for half or more of the U.S. anthropogenic methane source. However, current bottom-up inventories contain inherent uncertainties from extrapolating limited in situ measurements to larger scales. Here, we employ new airborne methane measurements over the U.S. Corn Belt and Upper Midwest, among the most intensive agricultural regions in the world, to quantify emissions from an array of key agriculture and waste point sources. Nine of the largest concentrated animal feeding operations in the region and two sugar processing plants were measured, with multiple revisits during summer (August 2017), winter (January 2018), and spring (May-June 2018). We compare the top-down fluxes with state-of-science bottom-up estimates informed by U.S. Environmental Protection Agency methodology and site-level animal population and management practices. Top-down point source emissions are consistent with bottom-up estimates for beef concentrated animal feeding operations but moderately lower for dairies (by 37% on average) and significantly lower for sugar plants (by 80% on average). Swine facility results are more variable. The assumed bottom-up seasonality for manure methane emissions is not apparent in the aircraft measurements, which may be due to on-site management factors that are difficult to capture accurately in national-scale inventories. If not properly accounted for, such seasonal disparities could lead to source misattribution in top-down assessments of methane fluxes.

Seasonal disparities in airborne lead (Pb) and associated foliar uptake by ryegrass (Lolium perenne L.): A Pb isotopic approach

Foliar uptake of airborne lead (Pb) may be particularly important for Pb accumulation in plant organs. However, the aerosol bioconcentration factor (BCF) in different seasons has seldom been reported. In the present study, we collected ryegrass (Lolium perenne L.) and size-segregated aerosols (SSA) during the corresponding growing seasons, and analyzed these for both Pb concentrations and isotopic ratios. Airborne Pb showed a seasonally varying concentration that was approximately 20% higher in winter than in spring. The bioavailability index, however, was higher in spring. Coupling the stable isotope technique with the bioavailable Pb of aerosol was more reliable in identifying airborne Pb accumulation in leaves than the total determination, suggesting that the hydrophilic absorption pathway was probably dominant for the foliar uptake of Pb in ryegrass. Contributions of airborne Pb accumulation were 88%–92% for washed ryegrass growing outdoors, indicating that the foliar uptake of Pb in the field was mainly from atmospheric deposition. The aerosol BCF of Pb for ryegrass was 6.4–11.4 m3/g in winter and 22.9–31.5 m3/g in spring. The increased aerosol BCF in spring was due to the suitable temperature, abundant rainfall, and increased Pb solubility of the aerosol. Therefore, our results indicate that, for the foliar uptake of Pb, both the aerosol Pb concentration, composition, and seasonal influence should be considered.

Metabolic rate does not vary with seasonal change in Anopheles arabiensis adults in South Africa

An important component of South Africa’s malaria elimination agenda is identifying the entomological drivers of residual transmission, especially those that present opportunities for enhanced vector control. Seasonal mosquito density correlates directly with malaria transmission in South Africa. Transmission is highest during the warm rainy season and lowest but not entirely absent during the cooler dry season. The factors that sustain dry-season mosquito survival remain unknown. The aim of this project was therefore to investigate seasonal change in metabolic rate to determine the presence or absence of winter dormancy in malaria vector mosquitoes. Metabolic rate, determined by CO2 production during closed-system respirometry, was measured from wild anophelines collected from KwaZulu-Natal Province, South Africa. Monthly sampling spanned all four seasons (summer, autumn, winter, and spring) in 2017. Anopheles arabiensis and An. parensis specimens formed the majority of the total 437 identified specimens (n = 216 and n = 162, respectively). Metabolic rate data from wild-caught mosquitoes showed no significant seasonal disparities for An. arabiensis and An. parensis males and females. Further laboratory experiments assessed the effect of manipulated photoperiod, representing seasonal day-length changes, on the metabolic rate of colonized An. arabiensis mosquitoes. Simulations of midwinter (10 h:14 h light dark) and midsummer (14 h:10 h) day-length showed no significant effect on the metabolic rate of these mosquitoes. Age (in days) had a significant effect on the metabolic rate of both male and female colonized adult An. arabiensis mosquitoes which may be linked to developmental factors during maturation of adults. These data suggest that the South African populations of the malaria vector species An. arabiensis and An. parensis do not curtail their breeding and foraging activities during the colder and drier winter months. Overwintering by diapause does not appear to be triggered in the adult mosquito stage in An. arabiensis. However, their respective population densities do decrease considerably during winter leading to reduced malaria transmission and the opportunity for control by winter larviciding of known breeding sites.

Spatiotemporal variation in primary nutrients amassing in peninsular river sediment: India

Damodar is an important river of the industrial state Jharkhand, India. It menders through various coal mining regions is now under serious threats due to industrial and sewage discharge which is rich in TOC, available nitrogen, phosphorus and potassium (primary nutrient plants). Hence, present research work was formulated with the hypothesis of “river sediment possess high concentration of primary nutrients which can be used in mine site reclamation”. In this regard, spatiotemporal and seasonal variations in concentration of these nutrients were studied to know its sources. Nutrient index of the sediment was assessed to compare its fertility with soil for its utilization in mine site reclamation and pollution control of the river. Study area has momentous spatial variation; Garga river (site 12) carry sewage and effluent from Bokaro steel city having maximum concentration of TOC, available N, and P, while available K was maximum at Chandrapura thermal power plant (site 9). Substantial seasonal and temporal disparity in nutrient concentration was also found. In 2014, TOC (0.82 and 0.5)%, available N (12.3 mg/kg and 7.9 mg/kg), P (11.3 mg/kg and 6.2 mg/kg), and available K was (218 mg/kg and 201.7 mg/kg), respectively, in pre- and post-monsoon. These amplified to 39% for TOC, 19–29% for nitrogen, more than 35% for phosphorus and 2.9–3.9% for K by 2016. The nutrient index was medium for TOC and pH except low in 2014 post-monsoon. Nutrient index for nitrogen was low while high for phosphorus and potassium during the study.

The mechanisms behind changes in the seasonality of global precipitation found in reanalysis products and CMIP5 simulations

As the global atmosphere warms, water vapor concentrations increase with rising temperatures at a rate of 7%/K. Precipitation change is associated with increased moisture convergence, which can be decomposed into thermodynamic and dynamic contributions. Our previous studies involving Coupled Model Intercomparison Project Phase 3 (CMIP3) projections have suggested that seasonal disparity in changes of global precipitation is primarily associated with the thermodynamic contribution. In this study, a vertically integrated atmospheric water budget analysis using multiple reanalysis datasets demonstrated that dynamic changes played a significant role in seasonal precipitation changes during 1979–2008, especially in the global average and ocean average. The thermodynamic component exhibited almost consistent magnitude in the contribution of seasonal precipitation changes during 1979–2008 in both CMIP5_AMIP models and reanalysis datasets, whereas the dynamic component (related to the tendency of ω and water vapor climatology) made a lower or negative contribution in the CMIP5_AMIP models compared with the reanalysis datasets. Strengthened (weakened) ascending and descending motions in the reanalysis datasets (CMIP5_AMIP models), which were indicative of strengthened (weakened) seasonal mean circulation, tended to increase (reduce) precipitation in the wet season and reduce (increase) precipitation in the dry season during the study period. Vertical profiles of the tendency of moist static energy in the mid-to-upper troposphere suggested a trend toward stability in the CMIP5_AMIP models and one toward instability in the reanalysis datasets. Such disagreement in stability might be related to the different warming tendency in the mid-to-upper troposphere over the tropics.

An investigation into sky temperature estimation, its variation, and significance in heat transfer calculations of solar cookers

AbstractThe determination of sky temperature assumes great importance in engineering applications such as radiative cooling of buildings. Many studies that involve a radiative exchange with the sky employ different reported models of sky temperature interchangeably. However, until now, hardly any systematic study has been done to quantify the errors/variations that might be encountered in calculating this radiative exchange employing these different correlations. In the current paper, first, a thorough analysis has been presented on the sky temperature correlations and a possible range of variation in sky temperature based on the estimation of sky emissivity is computed. Both diurnal‐nocturnal variation in sky temperature and seasonal disparities in sky temperature have been reported. Next, the case of a box‐type solar cooker has been taken up for investigation with respect to the possible influence of the sky temperature estimation in predicting its performance parameter, first‐figure‐of‐merit on a daily, seasonal, and climatic basis. Our observations show an enormous difference in sky temperature depending upon the expressions of emissivity from which it is derived. The variability of sky temperature has a nominal influence on the prediction of first‐figure‐of‐merit, although a marked discrepancy is observed across the seasons at the same location.

The impact of seasonality on multi-scale feature extraction techniques

ABSTRACTA key component in constructing a broad-scale, gridded population dataset is fine resolution geospatial data accurately depicting the extent of human activity. Analogous datasets are often developed using a wide range of methods and classification techniques, including the use of spatial features, spectral features, or the coupling of both to identify the presence of man-made structures from high-resolution satellite imagery. By using spatial and textural-based descriptors to generate high-resolution settlement layers for two dissimilar regions at the peak of seasonal disparity, this study attempts to quantify the influence of seasonality on the accuracy of a supervised, multi-scale, feature extraction framework for automated delineation of human settlement. Results generated by numerous models are evaluated against a reference dataset allowing for assessment of seasonal and feature differences in the context of accuracy. Global or regional mapping of human settlement requires the assemblage of high-resolution satellite images with variegated acquisition characteristics (season, sun elevation, off-nadir, etc.) to produce a cloud-free composite image from which features are extracted. Results of this study suggest an emphasis on imagery criteria, in particular acquisition date, could improve classification accuracy when mapping human settlement at scale.

Analysis and Predictions of Seasonal Affected Weather Variables of Bangladesh: SARIMA Models vs. Traditional Models

Bangladesh is a semi-tropical country, categorized by widespread seasonal disparities in rainfall, temperature, and humidity. Seasonality has been an input aspect of time series modeling when taking into account weather variables. In terms of multiple features of the weather variables i.e. randomness, cyclical variation and trend, time series methods etc. ARIMA can be a superior preference but, weather variables are affected by seasonality. Thinking about the grimy meadow, this paper presents Seasonal Auto-regressive Moving Average (SARIMA) model that takes seasonal and cyclical variation over the years. This study also aims to compare traditional methods like Single Exponential Method, Double Exponential Method, and Holt Winter Method with the SARIMA model. Time series plots, month plots, and B-B plots are used for identifying seasonal effect clearly. For seasonal stationary checking, Canova Hansen Stationary test has been utilized. Then, the order of the variables is identified, ACF and PACF have been checked and estimated preeminent order for these variables by AIC and Log-likelihood. Finally, Single Exponential Method, Double Exponential Method, and Holt Winter Method are introduced for comparing and forecasting. The proposed models SARIMA(0,0,0)(1,0,3)12, SARIMA(0,0,0)(1,0,1)12, SARIMA(0,0,0)(1,0,2)12 and SARIMA(0,0,0)(1,0,1)12 for maximum and minimum temperature, rainfall and humidity on the basis of Akaike Information Criteria and Log likelihood have been captured most seasonality of the data. Comparing them with traditional methods, traditional methods give a better result than the acquired model based on error measurement. So, traditional methods give a better estimate than the SARIMA models for selected weather variables, with lower mean square error, RMSE, MAE and MASE.

Seasonal Disparities in Airborne Bacteria and Associated Antibiotic Resistance Genes in PM2.5 between Urban and Rural Sites

The atmosphere represents an unappreciated compartment for the environmental dissemination of antibiotic resistance genes (ARGs), particularly via airborne fine particles (PM2.5), with strong implications for the inhalational exposure of the general population. We examined the seasonal variations in airborne bacteria and several ARGs in PM2.5 across an industrial–urban–rural transect in a megacity of China over an annual cycle. Seasonality was most apparent at the rural site with a remarkable wintertime reduction in the total level of bacteria and an enrichment of certain ARGs in winter but dilution in spring. This contrasted with the relative consistency across seasons at urban and industrial sites. The statistical correlation between ARGs and the mobile genetic element (MGE), intI1, weakened from rural to urban and industrial sites, which hints at the diluting role of intI1 in horizontal gene transfers across the land use gradient. Differing mechanisms may regulate site-specific population exposure to t...

Seasonality in Indian Stock Market: Delusion or Reality

In business, seasonal variations in production and sales are a recognised element. Hence, managers often try to calculate the fluctuations in demand and predict a more accurate nature of supply schedules. Seasonality in stock returns refers to a time series data that shows a systematic and repetitive variation over a span of a year that happens periodically. The primary root of seasonal disparities is the change in climate. The objective of this paper is to investigate whether the ‘day-of-the-week’ effect is prevailing in the Indian stock market returns. Empirical evidence of the existence of seasonality and thereby consistent abnormal returns have been identified in the efficient market postulate. One noteworthy inconsistency revealed was abnormal rates of return related to the calendar months. Several studies in this field empirically verified tests and found the existence of such anomalies on the stock return data that majorly refined to theories that turn of the year, the day-of-the-week and holiday effect has steadily created abnormal returns in both the developed and emerging markets. This paper tries to enrich the existing knowledge and to inspect whether cyclical irregularities continue in the emerging markets like India. The study provides the evidence that the presence of the day-of-the-week effects, precisely Monday effect and Friday effect, have disappeared in the Indian capital markets.

Diurnal and day-to-day characteristics of ambient particle mass size distributions from HR-ToF-AMS measurements at an urban site and a suburban site in Hong Kong

Abstract. Mass-concentration-based particle size distributions measured by a high-resolution aerosol mass spectrometer were systematically analyzed to assess long and short-term temporal characteristics of ambient particle size distributions sampled at a typical urban environment close to emission sources and a suburban coastal site representing a regional and local pollution receptor location in Hong Kong. Measured distributions were bimodal and deconvoluted into submodes, which were analyzed for day-to-day variations and diurnal variations. Traffic and cooking emissions at the urban site contributed substantially to particle mass in both modes, while notable decreases in mass median diameters were limited to the morning rush hour. Inorganic particle components displayed varying diurnal behavior, including nocturnal nitrate formation and daytime photochemical formation evident in both modes. Suburban particle size distributions exhibited notable seasonal disparities with differing influence of local formation, particularly in spring and summer, and transport which dominated in the fall season leading to notably higher sulfate and organic accumulation-mode particle concentrations. Variations in particle mixing state were evaluated by comparison of interspecies mass median diameter trends at both measurement sites. Internal mixing was prevalent in the accumulation mode in spring at the urban site, while greater frequency of time periods with external mixing of particle populations comprising different fractions of organic constituents was observed in summer. At the suburban site, sulfate and nitrate in the accumulation mode more frequently exhibited differing particle size distributions in all seasons, signifying a greater extent of external mixing. At the urban site, periods of greater submicron inorganic mass concentrations were more likely to be caused by increases in both Aitken- and accumulation-mode particle mass in summer, while at the suburban receptor location, organic and nitrate Aitken-mode particle mass contributed more regularly to higher total submicron species mass concentrations in most seasons (spring, summer, and winter).

Biology and ecology of the vulnerable holothuroid, Stichopus herrmanni, on a high-latitude coral reef on the Great Barrier Reef

Tropical aspidochirotid holothuroids are among the largest coral reef invertebrates, but gaps remain in our understanding of their ecological roles in lagoon sediment habitats, a vast component of coral-reef ecosystems. Stichopus herrmanni, listed as vulnerable (IUCN), is currently a major fishery species on the Great Barrier Reef (GBR) and throughout the Indo-Pacific. It is critical to characterise how this species interacts with its environment to understand how its removal may impact ecosystem functionality. We investigated seasonal variation in movement, bioturbation, feeding and gonad development of S. herrmanni over 3 yr at One Tree Reef, which has been a no-take area for decades. We determined the direct influence of the deposit-feeding activity of S. herrmanni on sediment turnover and granulometry, and on the abundance of infauna and benthic productivity in a comprehensive in situ analysis of tropical holothuroid feeding ecology. This species is highly mobile with identifiable individuals exhibiting site fidelity over 3 yr. With the potential to turn over an estimated 64–250 kg individual−1 yr−1, S. herrmanni is a major bioturbator. Stichopus herrmanni is a generalist feeder and influences trophic interactions by altering the abundance of infauna and microalgae. Stichopus herrmanni exhibited decreased feeding activity and gonad development in winter, the first documentation of a seasonal disparity in the bioturbation activity of a tropical holothuroid. Sediment digestion and dissolution by S. herrmanni has the potential to influence seawater chemistry, a particularly important feature in a changing ocean. Our results provide essential baseline data on the functional roles of this ecologically important species to inform development of ecosystem-based beche-de-mer fisheries management on the GBR.

Seasonal disparity in the co-occurrence of arsenic and fluoride in the aquifers of the Brahmaputra flood plains, Northeast India

Arsenic (As) and fluoride (F−) in groundwater are increasing global water quality and public health concerns. The present study provides a deeper understanding of the impact of seasonal change on the co-occurrence of As and F−, as both contaminants vary with climatic patterns. Groundwater samples were collected in pre- and post-monsoon seasons (n = 40 in each season) from the Brahmaputra flood plains (BFP) in northeast India to study the effect of season on As and F− levels. Weathering is a key hydrogeochemical process in the BFP and both silicate and carbonate weathering are enhanced in the post-monsoon season. The increase in carbonate weathering is linked to an elevation in pH during the post-monsoon season. A Piper diagram revealed that bicarbonate-type water, with Na+, K+, Ca2+, and Mg2+ cations, is common in both seasons. Correlation between Cl− and NO3 − (r = 0.74, p = 0.01) in the post-monsoon indicates mobilization of anthropogenic deposits during the rainy season. As was within the 10 µg L−1 WHO limit for drinking water and F− was under the 1.5 mg L−1 limit. A negative correlation between oxidation reduction potential and groundwater As in both seasons (r = −0.26 and −0.49, respectively, for pre-monsoon and post-monsoon, p = 0.05) indicates enhanced As levels due to prevailing reducing conditions. Reductive hydrolysis of Fe (hydr)oxides appears to be the predominant process of As release, consistent with a positive correlation between As and Fe in both seasons (r = 0.75 and 0.73 for pre- and post-monsoon seasons, respectively, at p = 0.01). Principal component analysis and hierarchical cluster analysis revealed grouping of Fe and As in both seasons. F− and sulfate were also clustered during the pre-monsoon season, which could be due to their similar interactions with Fe (hydr)oxides. Higher As levels in the post-monsoon appears driven by the influx of water into the aquifer, which drives out oxygen and creates a more reducing condition suitable for reductive dissolution of Fe (hydr)oxides. An increase in pH promotes desorption of As oxyanions AsO4 3− (arsenate) and AsO3 3− (arsenite) from Fe (hydr)oxide surfaces. Fluoride appears mainly released from F−-bearing minerals, but Fe (hydr)oxides can be a secondary source of F−, as suggested by the positive correlation between As and F− in the pre-monsoon season.

Occurrence and source apportionment of Per- and poly-fluorinated compounds (PFCs) in North Canal Basin, Beijing.

Various per- and poly-fluorinated compounds (PFCs) were first systematically investigated in North Canal Basin, Beijing, China. A total of 68 surface water samples were collected from North Canal Basin, Beijing, at high spatial resolution. The seasonal disparity was compared and associated with source variation. PFCs concentrations in low-water period ranged from 26 to 207 ng/L, and significantly declined levels were found in high-water period. The individual component proportions among different sites varied less in high-water period, when runoff played a role in mixing and diluting PFCs. A methodology combined with principal component analysis (PCA), heat map-hierarchical cluster analysis (HM-HCA), and correlation analysis were introduced to discriminate sources of PFCs in surface water. The statistical results agreed with each other, and daily domestic consumption, fire-fighting products and related industries were identified as sources of PFCs in this region. In addition, two composition ratios were proposed through the methodology to distinguish the impact of nonpoint source, and the outcome demonstrates that great disparities exist in compositional profiles between nonpoint source and others. Overall, the results showed that this comprehensive analysis method has great potential for source apportionment in surface water and other environmental compartments.

Assessment of Seasonal Disparity on Hydrogeochemical Facies Distribution in Cooum River, India

Assessment of Seasonal Disparity on Hydrogeochemical Facies Distribution in Cooum River, India