Satellites reveal widespread decline in global lake water storage.

Climate change and human activities increasingly threaten lakes that store 87% of Earth's liquid surface fresh water. Yet, recent trends and drivers of lake volume change remain largely unknown globally. Here, we analyze the 1972 largest global lakes using three decades of satellite observations, climate data, and hydrologic models, finding statistically significant storage declines for 53% of these water bodies over the period 1992-2020. The net volume loss in natural lakes is largely attributable to climate warming, increasing evaporative demand, and human water consumption, whereas sedimentation dominates storage losses in reservoirs. We estimate that roughly one-quarter of the world's population resides in a basin of a drying lake, underscoring the necessity of incorporating climate change and sedimentation impacts into sustainable water resources management.

Investigating regime shifts and the factors controlling Total Inorganic Nitrogen concentrations in treated wastewater using non-homogeneous Hidden Markov and multinomial logistic regression models.

Total Inorganic Nitrogen (TIN) in treated wastewaters: the sum of effluent ammonia-, nitrate- and nitrite-nitrogen, is a common regulatory measure of nitrogen removal. In many parts of the United States, regulatory agencies have reduced discharge limits for TIN, recognizing the environmental and health impacts of these species. However, many permit limits are based on annual average or median values, and because temporal variability in effluent TIN is common, may not achieve water quality goals. We created a performance-based modeling approach using Hidden Markov Models and multinomial logistic regression using weekly effluent water quality data from an operating wastewater treatment facility in the US, over the period of January 1, 2010-March 31, 2014. In the two-step modeling approach, Hidden Markov Models capture temporal regime shifts in effluent TIN and multinomial logistic regression identifies prominent factors associated with the regime shifts. Simulations from the proposed Hidden Markov Model and multinomial logistic regression indicate that climate factors (temperature and precipitation), seasonality, effluent total ammonia nitrogen (TAN), and prior weeks' levels of effluent TIN are predictive of effluent TIN concentrations. The hybrid HMM-regression model correctly predicted the states of compliance (state 1) and non-compliance (state 2) with TIN limits with 84% accuracy. Further analysis using model simulations suggest that although annual average or median limits for TIN are met, this plant had a >30% probability of exceeding the annual limit on a weekly time scale, and therefore may not be reliably effective in protecting receiving water quality.

Development of a gridded meteorological dataset over Java island, Indonesia 1985-2014.

We describe a gridded daily meteorology dataset consisting of precipitation, minimum and maximum temperature over Java Island, Indonesia at 0.125°×0.125° (~14 km) resolution spanning 30 years from 1985-2014. Importantly, this data set represents a marked improvement from existing gridded data sets over Java with higher spatial resolution, derived exclusively from ground-based observations unlike existing satellite or reanalysis-based products. Gap-infilling and gridding were performed via the Inverse Distance Weighting (IDW) interpolation method (radius, r, of 25 km and power of influence, α, of 3 as optimal parameters) restricted to only those stations including at least 3,650 days (~10 years) of valid data. We employed MSWEP and CHIRPS rainfall products in the cross-validation. It shows that the gridded rainfall presented here produces the most reasonable performance. Visual inspection reveals an increasing performance of gridded precipitation from grid, watershed to island scale. The data set, stored in a network common data form (NetCDF), is intended to support watershed-scale and island-scale studies of short-term and long-term climate, hydrology and ecology.

Assessment of wastewater treatment facility compliance with decreasing ammonia discharge limits using a regression tree model.

A regression tree-based diagnostic approach is developed to evaluate factors affecting US wastewater treatment plant compliance with ammonia discharge permit limits using Discharge Monthly Report (DMR) data from a sample of 106 municipal treatment plants for the period of 2004-2008. Predictor variables used to fit the regression tree are selected using random forests, and consist of the previous month's effluent ammonia, influent flow rates and plant capacity utilization. The tree models are first used to evaluate compliance with existing ammonia discharge standards at each facility and then applied assuming more stringent discharge limits, under consideration in many states. The model predicts that the ability to meet both current and future limits depends primarily on the previous month's treatment performance. With more stringent discharge limits predicted ammonia concentration relative to the discharge limit, increases. In-sample validation shows that the regression trees can provide a median classification accuracy of >70%. The regression tree model is validated using ammonia discharge data from an operating wastewater treatment plant and is able to accurately predict the observed ammonia discharge category approximately 80% of the time, indicating that the regression tree model can be applied to predict compliance for individual treatment plants providing practical guidance for utilities and regulators with an interest in controlling ammonia discharges. The proposed methodology is also used to demonstrate how to delineate reliable sources of demand and supply in a point source-to-point source nutrient credit trading scheme, as well as how planners and decision makers can set reasonable discharge limits in future.

Construction Safety Risk Modeling and Simulation.

By building on a genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data-driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute-based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency-magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety-related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.

Risk-Cost Estimation of On-Site Wastewater Treatment System Failures Using Extreme Value Analysis.

Owner resistance to increasing regulation of on-site wastewater treatment systems (OWTS), including obligatory inspections and upgrades, moratoriums and cease-and-desist orders in communities around the U.S. demonstrate the challenges associated with managing risks of inadequate performance of owner-operated wastewater treatment systems. As a result, determining appropriate and enforceable performance measures in an industry with little history of these requirements is challenging. To better support such measures, we develop a statistical method to predict lifetime failure risks, expressed as costs, in order to identify operational factors associated with costly repairs and replacement. A binomial logistic regression is used to fit data from public records of reported OWTS failures, in Boulder County, Colorado, which has 14 300 OWTS to determine the probability that an OWTS will be in a low- or high-risk category for lifetime repair and replacement costs. High-performing or low risk OWTS with repairs and replacements below the threshold of $9000 over a 40-year life are associated with more frequent inspections and upgrades following home additions. OWTS with a high risk of exceeding the repair cost threshold of $18 000 are further analyzed in a variation of extreme value analysis (EVA), Points Over Threshold (POT) where the distribution of risk-cost exceedance values are represented by a generalized Pareto distribution. The resulting threshold cost exceedance estimates for OWTS in the high-risk category over a 40-year expected life ranged from $18 000 to $44 000.

Modeling on-site wastewater treatment system performance fragility to hydroclimate stressors.

Increasing variability of climate-related factors, especially precipitation and temperature, poses special risks to on-site wastewater treatment systems (OWTS), which depend on subsurface saturation conditions for treatment and dispersion of wastewater. We assess OWTS fragility - the degree to which a system loses functionality - as a step to characterizing the resilience of residential wastewater treatment systems. We used the frequency and indexed severity of OWTS failures and resulting repairs to quantify fragility as a function of hydroclimate variables, including precipitation, temperature and stream flow. The frequency of each category of repair (minor, moderate and major) for 225 OWTS obtained from Boulder County public health records was modeled as a function of climate factors using a generalized linear model with a Poisson distribution link function. The results show that prolonged precipitation patterns, with monthly rainfall >10.16 cm, influence OWTS fragility, and complete loss of OWTS functionality, requiring replacement, is impacted by high temperatures, frequency of wetter-than-normal months, and the magnitude of peak stream flow in the watershed. Weather-related covariates explained 70% of the variability in OWTS major repair data between 1979 and 2006. These results indicate that fragility arising from climate factors, and associated costs to owners, environmental and health impacts, should be considered in planning, design and operation of OWTS.

Climate Change and the Emergent Epidemic of CKD from Heat Stress in Rural Communities: The Case for Heat Stress Nephropathy.

Climate change has led to significant rise of 0.8°C-0.9°C in global mean temperature over the last century and has been linked with significant increases in the frequency and severity of heat waves (extreme heat events). Climate change has also been increasingly connected to detrimental human health. One of the consequences of climate-related extreme heat exposure is dehydration and volume loss, leading to acute mortality from exacerbations of pre-existing chronic disease, as well as from outright heat exhaustion and heat stroke. Recent studies have also shown that recurrent heat exposure with physical exertion and inadequate hydration can lead to CKD that is distinct from that caused by diabetes, hypertension, or GN. Epidemics of CKD consistent with heat stress nephropathy are now occurring across the world. Here, we describe this disease, discuss the locations where it appears to be manifesting, link it with increasing temperatures, and discuss ongoing attempts to prevent the disease. Heat stress nephropathy may represent one of the first epidemics due to global warming. Government, industry, and health policy makers in the impacted regions should place greater emphasis on occupational and community interventions.

Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.

To control disinfection byproduct (DBP) formation in drinking water, an understanding of the source water total organic carbon (TOC) concentration variability can be critical. Previously, TOC concentrations in water treatment plant source waters have been modeled using streamflow data. However, the lack of streamflow data or unimpaired flow scenarios makes it difficult to model TOC. In addition, TOC variability under climate change further exacerbates the problem. Here we proposed a modeling approach based on local polynomial regression that uses climate, e.g. temperature, and land surface, e.g., soil moisture, variables as predictors of TOC concentration, obviating the need for streamflow. The local polynomial approach has the ability to capture non-Gaussian and nonlinear features that might be present in the relationships. The utility of the methodology is demonstrated using source water quality and climate data in three case study locations with surface source waters including river and reservoir sources. The models show good predictive skill in general at these locations, with lower skills at locations with the most anthropogenic influences in their streams. Source water TOC predictive models can provide water treatment utilities important information for making treatment decisions for DBP regulation compliance under future climate scenarios.

Effect of average flow and capacity utilization on effluent water quality from US municipal wastewater treatment facilities.

There is increasing interest in decentralization of wastewater collection and treatment systems. However, there have been no systematic studies of the performance of small treatment facilities compared with larger plants. A statistical analysis of 4 years of discharge monthly report (DMR) data from 210 operating wastewater treatment facilities was conducted to determine the effect of average flow rate and capacity utilization on effluent biochemical oxygen demand (BOD), total suspended solids (TSS), ammonia, and fecal coliforms relative to permitted values. Relationships were quantified using generalized linear models (GLMs). Small facilities (40 m³/d) had violation rates greater than 10 times that of the largest facilities (400,000 m³/d) for BOD, TSS, and ammonia. For facilities with average flows less than 40,000 m³/d, increasing capacity utilization was correlated with increased effluent levels of BOD and TSS. Larger facilities tended to operate at flows closer to their design capacity while maintaining treatment suggesting greater efficiency.

Temporal patterns in daily measurements of inorganic and organic speciated PM2.5 in Denver.

Airborne particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) has been linked to a wide range of adverse health effects and as a result is currently regulated by the U.S. Environmental Protection Agency. PM2.5 originates from a multitude of sources and has heterogeneous physical and chemical characteristics. These features complicate the link between PM2.5 emission sources, ambient concentrations and health effects. The goal of the Denver Aerosol Sources and Health (DASH) study is to investigate associations between sources and health using daily measurements of speciated PM2.5 in Denver. The datxa set being collected for the DASH study will be the longest daily speciated PM2.5 data set of its kind covering 5.5 years of daily inorganic and organic speciated measurements. As of 2008, 4.5 years of bulk measurements (mass, inorganic ions and total carbon) and 1.5 years of organic molecular marker measurements have been completed. Several techniques were used to reveal long-term and short-term temporal patterns in the bulk species and the organic molecular marker species. All species showed a strong annual periodicity, but their monthly and seasonal behavior varied substantially. Weekly periodicities appear in many compound classes with the most significant weekday/weekend effect observed for elemental carbon, cholestanes, hopanes, select polycyclic aromatic hydrocarbons (PAHs), heavy n-alkanoic acids and methoxyphenols. Many of the observed patterns can be explained by meteorology or anthropogenic activity patterns while others do not appear to have such obvious explanations. Similarities and differences in these findings compared to those reported from other cities are highlighted.

Simulating ensembles of source water quality using a K-nearest neighbor resampling approach.

Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, we have developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing the United States Environmental Protection Agency's Information Collection Rule (ICR) data. In this technique, first an appropriate "feature vector" is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. We demonstrate this approach by applying it to simulate monthly ensembles of total organic carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities.

An open-source representation for 2-DE-centric proteomics and support infrastructure for data storage and analysis.

BACKGROUND: In spite of two-dimensional gel electrophoresis (2-DE) being an effective and widely used method to screen the proteome, its data standardization has still not matured to the level of microarray genomics data or mass spectrometry approaches. The trend toward identifying encompassing data standards has been expanding from genomics to transcriptomics, and more recently to proteomics. The relative success of genomic and transcriptomic data standardization has enabled the development of central repositories such as GenBank and Gene Expression Omnibus. An equivalent 2-DE-centric data structure would similarly have to include a balance among raw data, basic feature detection results, sufficiency in the description of the experimental context and methods, and an overall structure that facilitates a diversity of usages, from central reposition to local data representation in LIMs systems. RESULTS & CONCLUSION: Achieving such a balance can only be accomplished through several iterations involving bioinformaticians, bench molecular biologists, and the manufacturers of the equipment and commercial software from which the data is primarily generated. Such an encompassing data structure is described here, developed as the mature successor to the well established and broadly used earlier version. A public repository, AGML Central, is configured with a suite of tools for the conversion from a variety of popular formats, web-based visualization, and interoperation with other tools and repositories, and is particularly mass-spectrometry oriented with I/O for annotation and data analysis.

Unraveling the mystery of Indian monsoon failure during El Niño .

The 132-year historical rainfall record reveals that severe droughts in India have always been accompanied by El Niño events. Yet El Niño events have not always produced severe droughts. We show that El Niño events with the warmest sea surface temperature (SST) anomalies in the central equatorial Pacific are more effective in focusing drought-producing subsidence over India than events with the warmest SSTs in the eastern equatorial Pacific. The physical basis for such different impacts is established using atmospheric general circulation model experiments forced with idealized tropical Pacific warmings. These findings have important implications for Indian monsoon forecasting.

Data mining to support simulation modeling of patient flow in hospitals.

Spiraling health care costs in the United States are driving institutions to continually address the challenge of optimizing the use of scarce resources. One of the first steps towards optimizing resources is to utilize capacity effectively. For hospital capacity planning problems such as allocation of inpatient beds, computer simulation is often the method of choice. One of the more difficult aspects of using simulation models for such studies is the creation of a manageable set of patient types to include in the model. The objective of this paper is to demonstrate the potential of using data mining techniques, specifically clustering techniques such as K-means, to help guide the development of patient type definitions for purposes of building computer simulation or analytical models of patient flow in hospitals. Using data from a hospital in the Midwest this study brings forth several important issues that researchers need to address when applying clustering techniques in general and specifically to hospital data.