scholarly journals “Waste Not, Want Not” — Leveraging Sewer Systems and Wastewater-Based Epidemiology for Drug Use Trends and Pharmaceutical Monitoring

2021 ◽  
Author(s):  
Timothy B. Erickson ◽  
Noriko Endo ◽  
Claire Duvallet ◽  
Newsha Ghaeli ◽  
Kaitlyn Hess ◽  
...  
Keyword(s):  
Early Warning ◽  
Large Scale ◽  
Illicit Drugs ◽  
Drugs Of Abuse ◽  
Regional Scale ◽  
Early Warning Systems ◽  
Drug Consumption ◽  
Warning Systems ◽  
Opioid Use ◽  
Current Application

AbstractDuring the current global COVID-19 pandemic and opioid epidemic, wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring public health trends by analysis of biomarkers including drugs, chemicals, and pathogens. Wastewater surveillance downstream at wastewater treatment plants provides large-scale population and regional-scale aggregation while upstream surveillance monitors locations at the neighborhood level with more precise geographic analysis. WBE can provide insights into dynamic drug consumption trends as well as environmental and toxicological contaminants. Applications of WBE include monitoring policy changes with cannabinoid legalization, tracking emerging illicit drugs, and early warning systems for potent fentanyl analogues along with the resurging wave of stimulants (e.g., methamphetamine, cocaine). Beyond drug consumption, WBE can also be used to monitor pharmaceuticals and their metabolites, including antidepressants and antipsychotics. In this manuscript, we describe the basic tenets and techniques of WBE, review its current application among drugs of abuse, and propose methods to scale and develop both monitoring and early warning systems with respect to measurement of illicit drugs and pharmaceuticals. We propose new frontiers in toxicological research with wastewater surveillance including assessment of medication assisted treatment of opioid use disorder (e.g., buprenorphine, methadone) in the context of other social burdens like COVID-19 disease.

scholarly journals Climate Extreme Seeds a New Domoic Acid Hotspot on the US West Coast

2020 ◽  
Vol 2 ◽  
Author(s):  
Vera L. Trainer ◽  
Raphael M. Kudela ◽  
Matthew V. Hunter ◽  
Nicolaus G. Adams ◽  
Ryan M. McCabe
Keyword(s):  
Early Warning ◽  
West Coast ◽  
Domoic Acid ◽  
Large Scale ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Dungeness Crab ◽  
The Pacific ◽  
The Us ◽  
Us West

A heatwave that blanketed the northeast Pacific Ocean in 2013–2015 had severe impacts on the marine ecosystem through altered species composition and survival. A direct result of this marine heatwave was a sustained, record-setting harmful algal bloom (HAB), caused by the toxigenic diatom, Pseudo-nitzschia, that led to an unprecedented delay in harvest opportunity for commercial Dungeness crab (Metacarcinus magister) and closure of other recreational, commercial and tribal shellfish harvest, including razor clams. Samples collected during a cruise in summer 2015, showed the appearance of a highly toxic “hotspot” between Cape Mendocino, CA and Cape Blanco, OR that was observed again during cruises in the summers of 2016–2018. The transport of toxic cells from this retentive site northward during wind relaxations or reversals associated with storms resulted in economically debilitating delay or closure of Dungeness crab harvest in both northern California and Oregon in 2015–2019. Analyses of historic large-scale Pseudo-nitzschia HABs have shown that these events occur during warm periods such as El Niño, positive phases of the Pacific Decadal Oscillation, or the record-setting marine heatwave. In order to reduce the impacts of large-scale HABs along the west coast of North America, early warning systems have been developed to forewarn coastal managers. These early warning systems include the Pacific Northwest and California HAB Bulletins, both of which have documented elevated domoic acid and increased risk associated with the northern California hotspot. These early warnings enable mitigative actions such as selective opening of safe harvest zones, increased harvest limits during low risk periods, and early harvest in anticipation of impending HAB events. The aims of this study are to show trends in nearshore domoic acid along the US west coast in recent years, including the recent establishment of a new seed bed of highly-toxic Pseudo-nitzschia, and to explore how early warning systems are a useful tool to mitigate the human and environmental health and economic impacts associated with harmful algal blooms.

scholarly journals Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

2021 ◽  
Vol 15 (02) ◽  
pp. 11-17
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Sidi Ahmed Mahmoudi ◽  
Adriano Guttadauria ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Early Warning ◽  
Large Scale ◽  
Prediction Models ◽  
Warning System ◽  
Early Warning Systems ◽  
Economic Losses ◽  
Warning Systems ◽  
Large Scale Data ◽  
Set Up

Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

An early warning system for rainfall-triggered shallow slides and debris flows. Application in Catalonia, Spain and Canton of Bern, Switzerland

2020 ◽  
Author(s):  
Rosa M Palau ◽  
Marc Berenguer ◽  
Marcel Hürlimann ◽  
Daniel Sempere-Torres ◽  
Catherine Berger ◽  
...  
Keyword(s):  
Early Warning ◽  
Debris Flows ◽  
Risk Mitigation ◽  
Regional Scale ◽  
Slope Angle ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Radar Rainfall ◽  
Susceptibility Maps ◽  
Landslide Event

<p>Risk mitigation for rainfall-triggered shallow slides and debris flows at regional scale is challenging. Early warning systems are a helpful tool to depict the location and time of future landslide events so that emergency managers can act in advance. Recently, some of the regions that are recurrently affected by rainfall triggered landslides have developed operational landslide early warning systems (LEWS). However, there are still many territories where this phenomenon also represents an important hazard and lack this kind of risk mitigation strategy.</p><p>The main objective of this work is to study the feasibility to apply a regional scale LEWS, which was originally designed to work over Catalonia (Spain), to run in other regions. To do so we have set up the LEWS to Canton of Bern (Switzerland).</p><p>The LEWS combines susceptibility maps to determine landslide prone areas and in real time high-resolution radar rainfall observations and forecasts. The output is a qualitative warning level map with a resolution of 30 m.</p><p>Susceptibility maps have been derived using a simple fuzzy logic methodology that combines the terrain slope angle, and land use and land cover (LULC) information. The required input parameters have been obtained from regional, pan-European and global datasets.</p><p>Rainfall inputs have been retrieved from both regional weather radar networks, and the OPERA pan-European radar composite. A set of global rainfall intensity-duration data has been used to asses if a rainfall event has the potential of triggering a landslide event.</p><p>The LEWS has been run in the region of Catalonia and Canton of Bern for specific rainfall events that triggered important landslides. Finally, the LEWS performance in Catalonia has been assessed.</p><p>Results in Catalonia show that the LEWS performance strongly depends on the quality of both the susceptibility maps and rainfall data. However, in both regions the LEWS is generally able to issue warnings for most of the analysed landslide events.</p>

scholarly journals Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

2013 ◽  
Vol 17 (3) ◽  
pp. 1229-1240 ◽  
Author(s):  
G. Martelloni ◽  
S. Segoni ◽  
D. Lagomarsino ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Snow Accumulation ◽  
Point Of View ◽  
Warning Systems ◽  
Temperature Index ◽  
Landslide Early Warning ◽  
Optimal Values

Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation. After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

scholarly journals Feasibility of Tsunami Early Warning Systems for small volcanic islands

2009 ◽  
Vol 9 (6) ◽  
pp. 1911-1919 ◽  
Author(s):  
G. Bellotti ◽  
M. Di Risio ◽  
P. De Girolamo
Keyword(s):  
Physical Model ◽  
Early Warning ◽  
Model Experiment ◽  
Large Scale ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Tsunami Early Warning ◽  
Volcanic Islands ◽  
Tsunami Early Warning Systems ◽  
The Waves

Abstract. This paper investigates the feasibility of Tsunami Early Warning Systems for small volcanic islands focusing on warning of waves generated by landslides at the coast of the island itself. The critical concern is if there is enough time to spread the alarm once the system has recognized that a tsunami has been generated. We use the results of a large scale physical model experiment in order to estimate the time that tsunamis take to travel around the island inundating the coast. We discuss how and where it is convenient to place instruments for the measurement of the waves.

Machine Learning: potential for local and regional deep-seated landslide nowcasting

2020 ◽  
Author(s):  
Adriaan van Natijne ◽  
Roderik Lindenbergh ◽  
Thom Bogaard
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Early Warning ◽  
Satellite Remote Sensing ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Warning Systems

<p>Where landslide hazard mitigation is impossible, Early Warning Systems are a valuable alternative to reduce landslide risk. To this extent nowcasting and Early Warning Systems for landslide hazard have been implemented mostly at local scale. Unfortunately, such systems are often difficult to implement at regional scale or in remote areas due to dependency on local sensors. However, in recent years various studies have demonstrated the effective application of Machine Learning for deformation forecasting of slow-moving, deep-seated landslides. Machine Learning, combined with satellite Remote Sensing products offers new opportunities for both local and regional monitoring of deep-seated landslides and associated processes.</p><p>Working from the key variables of the landslide process we selected the available satellite Remote Sensing products, the necessary assumptions for a satellite only application and evaluated the potential benefit of local information. In the absence of continuous, satellite deformation measurements, nowcasting of the system state will provide a short term deformation prediction. We demonstrate the opportunities of Machine Learning on multi-sensor monitored Austrian landslide and anticipate on the integration in an Early Warning System. Furthermore, we highlight the risks and opportunities arising from the limited physics constraints in Machine Learning.</p>

scholarly journals A global approach to defining flood seasons

2015 ◽  
Vol 12 (4) ◽  
pp. 4595-4630
Author(s):  
D. Lee ◽  
P. Ward ◽  
P. Block
Keyword(s):  
Early Warning ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Global Approach ◽  
Flood Season ◽  
Flood Warning ◽  
Advanced Planning ◽  
Basin Scale

Abstract. Globally, flood catastrophes lead all natural hazards in terms of impacts on society, causing billions of dollars of damages annually. While short-term flood warning systems are improving in number and sophistication, forecasting systems on the order of months to seasons are a rarity, yet may lead to further disaster preparedness. To lay the groundwork for prediction, dominant flood seasons must be adequately defined. A global approach is adopted here, using the PCR-GLOBWB model to define spatial and temporal characteristics of major flood seasons globally. The main flood season is identified using a volume-based threshold technique. In comparison with observations, 40% (50%) of locations at a station (sub-basin) scale have identical peak months and 81% (89%) are within 1 month, indicating strong agreement between model and observed flood seasons. Model defined flood seasons are additionally found to well represent actual flood records from the Dartmouth Flood Observatory, further substantiating the models ability to reproduce the appropriate flood season. Minor flood seasons are also defined for regions with bi-modal streamflow climatology. Properly defining flood seasons can lead to prediction through association of streamflow with local and large-scale hydroclimatic indicators, and eventual integration into early warning systems for informed advanced planning and management. This is especially attractive for regions with limited observations and/or little capacity to develop early warning flood systems.

scholarly journals Snow Accumulation-Melting Model (SAMM) for integrated use in regional scale landslide early warning systems

2012 ◽  
Vol 9 (8) ◽  
pp. 9391-9423 ◽  
Author(s):  
G. Martelloni ◽  
S. Segoni ◽  
D. Lagomarsino ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Snow Accumulation ◽  
Point Of View ◽  
Warning Systems ◽  
Additional Advantage ◽  
Landslide Early Warning ◽  
Snow Model

Abstract. We propose a simple snow accumulation-melting model (SAMM) to be applied at the regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM follows an intermediate approach between physically based models and empirical temperature index models. It is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. The snow model validation gave satisfactory results; moreover we simulated an operational employment in a regional scale landslide early warning system (EWS) and found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

The CARE Telematics Network for the Surveillance of Influenza in Europe

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Surveillance Networks ◽  
Spanish Flu ◽  
The 1950S ◽  
National Networks ◽  
First Time

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.

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