Airborne influenza virus shedding by patients in health care units: Removal mechanisms affecting virus transmission.

In this study, we characterize the distribution of airborne viruses (influenza A/B) in hospital rooms of patients with confirmed infections. Concurrently, we monitored fine particulate matter (PM2.5 & PM10) and several physical parameters including the room air exchange rate, temperature, and relative humidity to identify corresponding correlations with virus transport and removal determinants. The results continue to raise concerns about indoor air quality (IAQ) in healthcare facilities and the potential exposure of patients, staff and visitors to aerosolized viruses as well as elevated indoor PM levels caused by outdoor sources and/or re-suspension of settled particles by indoor activities. The influenza A virus was detected in 42% of 33 monitored rooms, with viruses detectible up to 1.5 m away from the infected patient. Active coughing was a statistically significant variable that contributed to a higher positive rate of virus detection in the collected air samples. Viral load across patient rooms ranged between 222 and 5,760 copies/m3, with a mean of 820 copies/m3. Measured PM2.5 and PM10 levels exceeded IAQ daily exposure guidelines in most monitored rooms. Statistical and numerical analyses showed that dispersion was the dominant viral removal pathway followed by settling. Changes in the relative humidity and the room's temperature were had a significant impact on the viral load removal. In closure, we highlight the need for an integrated approach to control determinants of IAQ in patients' rooms.

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form.

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

Disaster waste management challenges and enabling factors for strategic planning: The case of the Beirut Port explosion.

Disasters occur in both developed and developing countries, generating large amounts of disaster waste including construction and demolition (C&D) waste that needs to be appropriately managed. While developed countries are capable of implementing adequate disaster waste management (DWM) strategies to facilitate their recovery processes, developing countries generally struggle to find the resources and expertise needed to develop such strategies. Lebanon is a developing country vexed by several systemic challenges that hindered its abilities to manage disaster waste. In this paper, we focus on the Beirut Port explosion (4 August 2020), which generated more than 800,000 tonnes of disaster wastes. This study first assesses the executed strategies and identifies their enabling factors and implementation challenges. It then proposes a framework for the proper management of disaster waste, which was validated through 18 in-depth interviews with experts and stakeholders involved in disaster management. Interview notes and transcripts were analyzed using an inductive-deductive process that allowed to identify themes using the constant comparative method. The data revealed that the main barriers toward implementing a successful DWM strategy were the absence of appropriate technologies, infrastructure, expertise, legislative framework and financial resources. The study concludes by proposing a DWM roadmap that includes contingency, risk reduction and implementation plans (IPs) that can enhance decision-making and ease the recovery process.

Analyzing eutrophication and harmful algal bloom dynamics in a deep Mediterranean hypereutrophic reservoir.

Excessive point and non-point nutrient loadings accompanied with elevated temperatures have increased the prevalence of harmful algal bloom (HAB). HABs pose significant environmental and public health concerns, particularly for inland freshwater systems. In this study, the eutrophication and HAB dynamics in the Qaraoun Reservoir, a hypereutrophic deep monomictic reservoir suffering from poor water quality, were assessed. The reservoir was mostly phosphorus limited, and large algal particulates dominated light attenuation in the water column. During bloom events, surface chlorophyll-a concentrations increased up to 961.3 µg/L, while surface concentrations of ammonia and ortho-phosphate were rapidly depleted; surface dissolved oxygen reached supersaturation levels and surface pH levels were up to 3 units higher than those measured in the hypolimnion. Meanwhile, measured Microcystin-LR toxin concentrations in the reservoir exceeded the World Health Organization 1 µg/L provisional guideline 45% of the times. Yet, the results showed that most of the toxins were intra-cellular, suggesting that they decayed rapidly when released into the reservoir. Results from a random forests ensemble model indicated that tracking the changes in surface dissolved oxygen levels, ammonium, ortho-phosphate, and pH can be an effective program towards predicting the reservoir's trophic state and algae blooms.

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions.

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

Electromyography analysis: Comparison of maximum voluntary contraction exercises for the latissimus dorsi.

BACKGROUND: A prerequisite for interpreting electromyography (EMG) data is to normalize them to a maximum voluntary contraction (MVC), expressing them as percentage of the MVC. OBJECTIVE: The objective of this study was to compare different recommended MVC exercises for the latissimus dorsi and identify the exercise that elicits the highest EMG amplitude. METHODS: Fifteen male participants were recruited. Five recommended MVC exercises were investigated, including chest-supported shoulder extension, prone shoulder extension, lat-pull down, abducted shoulder flexion, and internal shoulder rotation. RESULTS: Chest-supported shoulder extension reported the highest mean EMG, which was not significantly different from the mean EMG of prone shoulder extension. Both of these exercises led to significantly greater EMG means than the other exercises. Sixty percent of the participants achieved their highest EMG amplitude with chest-supported shoulder extension and 40% with prone shoulder extension. None of the other exercises elicited the highest EMG amplitude in any of the participants. CONCLUSIONS: Both chest-supported shoulder extension and prone shoulder extension proved to be effective MVC exercises, with the former showing more consistency in eliciting the highest EMG amplitude. If the true MVC is desired, the authors recommend performing both exercises and then using the overall highest EMG amplitude for normalization purposes.

Impacts of Climate Change and Heat Stress on Farmworkers' Health: A Scoping Review.

Due to the continuous rise of global temperatures and heatwaves worldwide as a result of climate change, concerns for the health and safety of working populations have increased. Workers in the food production chain, particularly farmworkers, are especially vulnerable to heat stress due to the strenuous nature of their work, which is performed primarily outdoors under poor working conditions. At the cross-section of climate change and farmworkers' health, a scoping review was undertaken to summarize the existing knowledge regarding the health impacts associated with climate change and heat stress, guide future research toward better understanding current and future climate change risks, and inform policies to protect the health and safety of agricultural workers. A systematic search of 5 electronic databases and gray literature websites was conducted to identify relevant literature published up until December 2021. A total of 9045 records were retrieved from the searches, of which 92 articles were included in the final review. The majority of the reviewed articles focused on heat-related illnesses (n = 57) and kidney diseases (n = 28). The risk factors identified in the reviewed studies included gender, dehydration, heat strain, wearing inappropriate clothing, workload, piece-rate payment, job decision latitude, and hot environmental conditions. On the other hand, various protective and preventive factors were identified including drinking water, changing work hours and schedule of activities, wearing appropriate clothing, reducing soda consumption, taking breaks in shaded or air-conditioned areas, and increasing electrolyte consumption in addition to improving access to medical care. This review also identified various factors that are unique to vulnerable agricultural populations, including migrant and child farmworkers. Our findings call for an urgent need to expand future research on vulnerable agricultural communities including migrant workers so as to develop effective policies and interventions that can protect these communities from the effects of heat stress.

Assessing the environmental risk and pollution status of soil and water resources in the vicinity of municipal solid waste dumpsites.

Municipal solid waste management remains a major challenge for many developing countries where unsanitary and environmentally damaging practices, such as open dumping and burning of wastes, are consistently utilized as means of waste disposal. This study aimed to assess the impact of local dumpsites in a region in Southern Lebanon and to assess/determine the level of pollution they cause on local ecosystems and the concomitant risks to public health. Accordingly, soil and water samples were collected from the seven dumpsites that were investigated over the course of two seasons. Several biological, chemical, and physical parameters were examined, with the results being utilized to calculate a number of environmental indices. Results indicated that several soil parameters including TN (700-2400 mg/kg), pH (8.3-8.7), COD (39-1995 mg/kg), and sulfate levels (17.8-301.6 mg/kg) were altered by the dumpsites. Heavy metal concentrations varied between dumpsites; however, the most commonly prevalent metals across all dumps were Fe (992-41,500 mg/kg), Cr (17.4-139.5 mg/kg), Zn (24.1-177.4 mg/kg), Cu (9.42-148.2 mg/kg), and Mn (25.2-776.5 mg/kg), though recorded concentrations exceeded permissible limits only in certain instances. Evidently, soil samples collected at dumpsites had higher concentrations compared to the samples collected away from dumpsites reaching 27 times more in certain locations. The altered parameters have a direct effect on soil fertility and, if biomagnified, could disrupt crop yields and impact human health. Physiochemical properties and heavy metal concentrations in water samples were not significantly altered and were found to be within permissible limits. However, it is crucial to develop a monitoring and remediation plan to decrease the percolation of leachate to water resources.

Assessing the intra-urban variability of nitrogen oxides and ozone across a highly heterogeneous urban area.

High-resolution air quality maps are critical towards assessing and understanding exposures to elevated air pollution in dense urban areas. However, these surfaces are rarely available in low- and middle-income countries that suffer from some of the highest air pollution levels worldwide. In this study, we make use of land use regressions (LURs) to generate annual and seasonal, high-resolution nitrogen dioxide (NO2), nitrogen oxides (NOx), and ozone (O3) exposure surfaces for the Greater Beirut Area (GBA) in Lebanon. NO2, NOx and O3 concentrations were monitored using passive samplers that were deployed at 55 pre-defined monitoring locations. The average annual concentrations of NO2, NOx, and O3 across the GBA were 36.0, 89.7, and 26.9 ppb, respectively. Overall, the performance of the generated models was appropriate, with low biases, high model robustness, and acceptable R2 values that ranged between 0.66 and 0.73 for NO2, 0.56 and 0.60 for NOx, and 0.54 and 0.65 for O3. Traffic-related emissions as well as the operation of a fossil-fuel power plant were found to be the main contributors to the measured NO2 and NOx levels in the GBA, whereas they acted as sinks for O3 concentrations. No seasonally significant differences were found for the NO2 and NOx pollution surfaces; as their seasonal and annual models were largely similar (Pearson's r > 0.85 for both pollutants). On the other hand, seasonal O3 pollution surfaces were significantly different. The model results showed that around 99% of the population of the GBA were exposed to NO2 levels that exceeded the World Health Organization defined annual standard.

The feasibility of solar-powered small-scale brackish water desalination units in a coastal aquifer prone to saltwater intrusion: A comparison between electrodialysis reversal and reverse osmosis.

In the face of increasing water shortages worldwide, water desalination has the potential to expand the available freshwater supply options in many water stressed regions. This paper assesses the feasibility of adopting photovoltaic powered small-scale brackish water desalination units in a coastal aquifer facing saltwater intrusion and chronic water shortages. Moreover, a detailed cost comparison, which incorporates the associated environmental costs, is conducted between the Electrodialysis Reversal (EDR) technique and Reverse Osmosis (RO). The results showed that PV-powered small-scale desalination units were more economically viable as compared to grid-powered units, when the electricity tariffs reflected non-subsidized electricity prices and environmental costs were internalized. EDR-PV proved to be more economically feasible and with a lower environmental footprint as compared to RO-PV, up until the salinity of the aquifer was below 5000 ppm (EDR: 0.57-1.18 $/m3; RO = 1.19-1.59 $/m3). Beyond that salinity, the RO-PV was found to be the more economically viable option, with costs reaching as high as 2.65 $/m3 at a salinity of 25,000 ppm. Overall, the environmental costs between the two technologies varied significantly, largely due to differences in the generated brine volume, with EDR having better efficiencies at lower salinity levels. Finally, the study highlighted the risk of accelerating saltwater intrusion as a result of the increased market penetration of solar-powered desalination units along vulnerable coastal aquifers.

Determinants of Waste Management Practices and Willingness to Pay for Improving Waste Services in a Low-Middle Income Country.

In many low and middle-income countries, solid waste management (SWM) systems remain weak and lack standardization. Moreover, these systems fail to account for citizen's insight on the proposed solid waste initiatives. This study aims to identify the main determinants of SWM practices in a low-middle income country while accounting for citizens' perceived knowledge, attitudes, structural barriers, and willingness to pay for different services. Three communities were thus selected with varying socioeconomic factors and where different SWM practices were adopted. A cross-sectional study based on an interviewer-administered questionnaire was conducted across the three areas. Our results showed that increased knowledge and awareness of proper SWM did not correlate with people's attitudes nor with their adoption of positive waste management practices, such as reusing, reducing, recycling, and sorting of waste. Nevertheless, the results showed that the presence of an effective SWM system in a community positively influenced people's attitudes. Structural determinants, including the lack of appropriate facilities and adequate infrastructure, weak public knowledge on sorting, recycling, and composting, as well as the absence of guiding policies, appeared to be core barriers hindering the adoption of sustainable waste management practices across the three communities. The results of this study highlight the importance of establishing integrated SWM systems in developing countries, as they appear to trigger positive behaviors by the serviced citizens.

Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management.

Saltwater intrusion (SWI) is a global coastal problem caused by aquifer overpumping, land-use change, and climate change impacts. Given the complex pathways that lead to SWI, coastal urban areas with poorly monitored aquifers are in need of probabilistic-based decision support tools that can assist in better understanding and predicting SWI, while exploring effective means for sustainable aquifer management. In this study, we develop a Bayesian Belief Network (BBN) to account for the complex interactions of climatic and anthropogenic processes leading to SWI, while relating the severity of SWI to associated socioeconomic impacts and possible adaptation strategies. The BBN is further expanded into a Dynamic Bayesian Network (DBN) to assess the temporal progression of SWI and account for the compounding uncertainties over time. The proposed DBN is then tested at a pilot coastal aquifer underlying a highly urbanized water-stressed metropolitan area along the Eastern Mediterranean coastline (Beirut, Lebanon). The results show that the future impacts of climate change are largely secondary when compared to the persistent water deficits. While both supply and demand management could halt the progression of salinity, the potential for reducing or reversing SWI is not evident. The indirect socioeconomic burden associated with aquifer salinity was observed to improve, albeit heterogeneously, with the application of various adaptation strategies; however, this was at a cost associated with the implementation and operation of these strategies. The proposed DBN acts as an effective decision support tool that can promote sustainable aquifer management in coastal regions through its robust representation of the main drivers of SWI and linking them to expected socioeconomic burdens and management options. Integr Environ Assess Manag 2021;17:202-220. © 2020 SETAC.

Drivers of seasonal and annual air pollution exposure in a complex urban environment with multiple source contributions.

Outdoor air pollution is a global health concern, but detailed exposure information is still limited for many parts of the world. In this study, high-resolution exposure surfaces were generated for annual and seasonal fine particulate matter (PM2.5), coarse particulate matter (PM10), and carbon monoxide (CO) for the Greater Beirut Area (GBA), Lebanon, an urban zone with a complex topography and multiple source contributions. Land use regression models (LUR) were calibrated and validated with monthly data collected from 58 locations between March 2017 and March 2018. The annual mean (±1 SD) concentrations of PM2.5, PM10, and CO across the monitoring locations were 68.1 (±15.7) µg/m3, 83.5 (±19.5) µg/m3, and 2.48 (±1.12) ppm, respectively. The coefficients of determination for LUR models ranged from 56 to 67% for PM2.5, 44 to 63% for the PM10 models, and 50 to 60% for the CO. LUR model structures varied significantly by season for both PM2.5 and PM10 but not for CO. Traffic emissions were consistently the main source of CO emissions throughout the year. The relative importance of industrial emissions and power generation sources towards predicted PM levels increased during the hot season while the contribution of the international airport diminished. Moreover, the complex topography of the study area along with the seasonal changes in the predominant wind directions affected the spatial predicted concentrations of all three pollutants. Overall, the predicted exposure surfaces were able to conserve the inter-pollution correlations determined from the field monitoring campaign, with the exception of the cold season. Our pollution surfaces suggest that the entire population of Beirut is regularly exposed to concentrations exceeding the World Health Organization (WHO) air quality standards for both PM2.5 and PM10.

Nutrient pollutant loading and source apportionment along a Mediterranean river.

Rivers are increasingly being subjected to increased anthropogenic pollution stresses that undermine their designated uses and negatively affect sensitive coastal regions. The degradation of river water quality is attributed to both point and nonpoint sources of pollution. In this study, we determine the relative contribution of point and nonpoint pollutant loads in the Beirut River basin, a poorly monitored seasonal Mediterranean river. Water quality samples were collected on a weekly basis over 2 consecutive years (2016 and 2017) from four sampling sites that represent a gradient of increasing urbanization. Flow-concentration models were first developed to estimate total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) loads reaching the different sub-basins. The performance of the regression models varied by location and by pollutant, with improved performance in the downstream sections (adjusted R2 66% for TP and 59% for TN). Loads were also determined using the Beale's ratio method, which generally underestimated the loads as compared with the regression-based models. The relative contribution of the nonpoint source loads were then quantified using the Open Nonpoint Source Pollution and Erosion Comparison Tool (OpenNSPECT). The results showed that point sources were the main cause of water quality impairment across the entire basin, with load contributions varying between 75% in the headwaters and 98% in the urbanized downstream sections. The adopted modeling approach in this study provides an opportunity to better understand pollutant load dynamics in poorly monitored basins and a mechanism to apportion pollution loads between point and nonpoint sources.

The implications of Simpson's paradox for cross-scale inference among lakes.

Using cross-sectional data for making ecological inference started as a practical means of pooling data to enable meaningful empirical model development. For example, limnologists routinely use sample averages from numerous individual lakes to examine patterns across lakes. The basic assumption behind the use of cross-lake data is often that responses within and across lakes are identical. As data from multiple study units across a wide spatiotemporal scale are increasingly accessible for researchers, an assessment of this assumption is now feasible. In this study, we demonstrate that this assumption is usually unjustified, due largely to a statistical phenomenon known as the Simpson's paradox. Through comparisons of a commonly used empirical model of the effect of nutrients on algal growth developed using several data sets, we discuss the cognitive importance of distinguishing factors affecting lake eutrophication operating at different spatial and temporal scales. Our study proposes the use of the Bayesian hierarchical modeling approach to properly structure the data analysis when data from multiple lakes are employed.

Assessing the transferability of landuse regression models for ultrafine particles across two Canadian cities.

Land use regression (LUR) models have been increasingly used to predict intra-city variations in the concentrations of different air pollutants. However, limited research assessing the transferability of these models between cities has been published to date. In this study, LUR models were generated for Ultra-Fine Particles (UFP) (<0.1 um) using data collected from mobile monitoring campaigns in two Canadian cities, Montreal and Toronto. City-specific models were first generated for each city before the models were transferred to the second city with and without recalibration. The calibrated transferred models showed only a slight decrease in performance, with the coefficient of determination (R2), dropping from 0.49 to 0.36 for Toronto and from 0.41 to 0.38 for Montreal. Transferring models between cities with no calibration resulted in low R2; 0.11 in Toronto and 0.18 in Montreal. Moreover, two additional models were generated by combining data from the two cities. The first combined model (CM1) assumed a spatially invariant effect of the predictors, while the second (CM2) relaxed the assumption of spatial invariance for some of the model coefficients. The performance of both combined models (R2 ranged between 0.41 for CM1 and 0.43 for CM2; root mean squared error (RMSE) ranged between 0.34 for CM1 and 0.33 for CM2) was found to be on par with the Toronto city-specific model and outperformed the Montreal model. The results of this study highlight that the UFP LUR models appear to support transferability of model structures between cities with similar geographical characteristics, with a minor drop in model fit and predictive skill.

Data-Worth Assessment for a Three-Dimensional Optimal Design in Nonlinear Groundwater Systems.

Groundwater model predictions are often uncertain due to inherent uncertainties in model input data. Monitored field data are commonly used to assess the performance of a model and reduce its prediction uncertainty. Given the high cost of data collection, it is imperative to identify the minimum number of required observation wells and to define the optimal locations of sampling points in space and depth. This study proposes a design methodology to optimize the number and location of additional observation wells that will effectively measure multiple hydrogeological parameters at different depths. For this purpose, we incorporated Bayesian model averaging and genetic algorithms into a linear data-worth analysis in order to conduct a three-dimensional location search for new sampling locations. We evaluated the methodology by applying it along a heterogeneous coastal aquifer with limited hydrogeological data that is experiencing salt water intrusion (SWI). The aim of the model was to identify the best locations for sampling head and salinity data, while reducing uncertainty when predicting multiple variables of SWI. The resulting optimal locations for new observation wells varied with the defined design constraints. The optimal design (OD) depended on the ratio of the start-up cost of the monitoring program and the installation cost of the first observation well. The proposed methodology can contribute toward reducing the uncertainties associated with predicting multiple variables in a groundwater system.

Hindcasting eutrophication and changes in temperature and storage volume in a semi-arid reservoir: a multi-decadal Landsat-based assessment.

In situ monitoring of freshwater systems is often constrained by cost and accessibility, particularly in developing countries and in remote areas. Satellite remote sensing is therefore increasingly being integrated with existing in situ water quality monitoring programs. In this study, we use the Landsat TM/ETM+ image record collected between 1984 and 2015 to track temporal changes in trophic status, chlorophyll-a levels, algal bloom incidences, water clarity, water temperature, and reservoir water volume in a poorly monitored hypereutrophic semi-arid reservoir. Historical reservoir water quality data are inferred from calibrated Landsat-based empirical algorithms. The results show that, although the reservoir has existed in a eutrophic to hypereutrophic state over the past 30 years, its water quality has significantly deteriorated in the most recent decade. Mean summer chlorophyll-a concentrations were found to have increased by around 163% between 1984 and 2015, while water clarity dropped by more than 58% over the same period. Statistically significant changes in surface water temperatures were also apparent for the month of August, with a cumulative increase of 1.24 °C over the 31-year study period. The rise in temperature appears to correlate with the incidence of Microcystis blooms observed in the reservoir over the past decade. On the other hand, the water volume in the reservoir was found to have been fairly stable over time, likely as a result of adaptive reservoir management. This study demonstrates the strength of using Landsat data to hindcast and quantify changes in water quality and quantity in poorly monitored freshwater systems.

Factors influencing the reuse of reclaimed water as a management option to augment water supplies.

The reuse of treated wastewater, whether direct or indirect, can raise public concerns as a result of the overall risk perception. As such, community acceptance plays a significant role in the implementation of alternative water systems. Public attitudes towards water reuse are highly influenced by perceived health risk, religious prohibition, political issues, and the degree of human contact with recycled water. In most of the Mediterranean countries, wastewater is reused to different extents either within planned or unplanned schemes. Unfortunately, there are few in-depth studies of the socio-cultural aspects of reuse projects in developing countries, and Lebanon is no exception. Accordingly, this research will comprehensively tackle the issue of public knowledge, perceptions, and acceptance from different perspectives in an effort to provide national baseline information on wastewater reuse that is needed for future regulatory and developmental projects. As such, a survey was developed, tested, and administered. Results showed an inverse relation between the degree of human contact with the treated wastewater and public acceptance. People were found to be more inclined towards reuse for purposes with minimal human contact such as landscaping and agriculture with opposition when it came to use for personal use. Moreover, the results showed a general lack of trust in governing institutions and authorities, which could explain perceived health risks and perceptions of risk resulting from system failure. Overall, the willingness to use treated wastewater was found to vary as a function of the "disgust factor," religious beliefs, and perceptions of a high risk towards contracting waterborne diseases. The disgust towards reuse of treated wastewater was found to be a strong predictor affecting willingness to reuse whereby those who believed it is disgusting to reuse treated wastewater were found to be on average, three times less likely to reuse treated wastewater as compared to those who did not declare disgust. Similarly, a significant association was found between religious beliefs and respondents' willingness to use treated wastewater. Interviewees who thought treated wastewater reuse is not religiously accepted were, on average, twice less likely to use treated wastewater as compared to those who did not believe that reuse contradicts with their religious beliefs. Additionally, people's perceptions on associating the spread of water-borne diseases with reuse were found to be significant. Those who perceived that reusing treated wastewater would lead to diseases and affect the human health, were on average, twice less likely to accept reuse. Developing a comprehensive strategy that integrates increasing awareness and knowledge, setting policies related to water reuse, building public trust and communication channels, increasing public participation/engagement in decision making, and developing a sustainable management framework is thus crucial before any investments are made in reclaimed water-reuse projects.

Indoor and Outdoor Radon Concentration Levels in Lebanon.

Lebanon's lung cancer rates, among the highest in the Arab region, contribute to the burden of noncommunicable diseases. A number of studies have shown that lung cancer risk increases when smokers vs. nonsmokers exposed to elevated radon levels are compared. This research employs indoor and outdoor space and time concentration surveys across Lebanon, where the smoking rate among the population is among the highest in the world. The distributional properties of measured radon concentration were shown to be lognormal with median indoor and outdoor concentrations of 17 and 10 Bq m, respectively. Standard deviation for indoor concentrations was 1.2 times smaller than its outdoor counterpart, suggesting that weather-related patterns affect outdoor radon concentration variability. No significant spatial association was detected across seasons for indoor and outdoor radon concentrations. Geographical location, proximity to faults, and housing construction material had no significant impact on outdoor and indoor radon concentration variations. When lognormal distributions were used to determine exceedance probability of the recommended reference radon concentration, they were smaller than 0.1%. While exhibiting high seasonal variability, the study shows that radon does not appear to be a public health concern in Lebanon.