Table 1-1. How TEVA supports the six basic vulnerability assessment elements. VA Basic Element TEVA Element Characterize water system Simulation of Incidents (development of EPANET network model) Identify and prioritize adverse impacts Simulation of Incidents Consequence Assessment Identify critical assets Consequence Assessment Assess likelihood of adverse impacts Simulation of Incidents Evaluation of existing countermeasures Threat Mitigation Analysis Develop risk reduction plan or actions Threat Mitigation Analysis Without specific intelligence information, one cannot predict exactly how terrorist groups might sabotage a water system. Therefore, the TEVA framework is based on a probabilistic analysis of a large number of likely contamination incidents. Although the number of possible variations on terrorist attacks is nearly infinite, by selecting a “large enough” set of likely incidents, the expected impacts of contamination incidents can be assessed. A single contamination incident can be defined by the type of contaminant, the amount and concentration of the contaminant, the location of the injection into the distribution system, and the start and stop time of the injection. A threat ensemble, then, is a large collection of distinct incidents. In the TEVA framework (as well as in previous work by Ostfeld et al. 2004), the vulnerability of a water system is based on an assessment of the entire threat ensemble. TEVA fits into the general VA structure as shown in Table 1-1.
Drinking Water Contamination Warning Systems Research on methods to mitigate the impacts of contamination incidents have converged over the last several years on the concept of a contamination warning system (CWS). CWSs have been proposed as a promising approach for the early detection and management of contamination incidents in drinking water distribution systems (ASCE 2004; AWWA 2005; U.S. EPA 2005a). EPA is piloting CWSs through the Office of Water’s Water Security (WS) Initiative, formerly called WaterSentinel, at a series of drinking water utilities. The key to an effective response to a water contamination incident is minimizing the time between detection of a contamination incident and implementation of effective response actions that mitigate further consequences. Implementation of a robust CWS can achieve this objective by providing an earlier indication of a potential contamination incident than would be possible in the absence of a CWS. A CWS is a proactive approach that uses advanced monitoring technologies and enhanced surveillance activities to collect, integrate, analyze, and communicate information that provides a timely warning of potential contamination incidents. The WS Initiative promotes a comprehensive CWS that is theoretically capable of detecting a wide range of contaminants, covering a large spatial area of the distribution system, and providing early detection in time to mitigate impacts (U.S. EPA 2005c). Components of the WS Initiative include: • Online water quality monitoring. Continuous online monitors for water quality parameters, such as chlorine residual, total organic carbon, electrical conductivity, pH, temperature, oxidation reduction potential, and turbidity help to establish expected baselines for these parameters in a given distribution system. Event detection systems, such as CANARY (Hart et al. 2007), can be used to detect anomalous changes from the baseline to provide an indication of potential contamination. Other monitoring technologies can be used as well, such as contaminant-specific monitors, although the goal is to detect a wide range of possible contaminants. • Consumer complaint surveillance. Consumer complaints regarding unusual taste, odor, or appearance of the water are often reported to water utilities, which track the reports as well as steps taken by the utility to address these water quality problems. The WS Initiative is developing a process to automate the compilation and tracking of information provided by consumers. Unusual trends that might be indicative of a contamination incident can be rapidly identified using this approach. • Public health surveillance. Syndromic surveillance conducted by the public health sector, including information such as unusual trends in over-the-counter sales of medication, as well as reports from emergency medical service logs, 911 call centers, and poison control hotlines might serve as a warning of a potential drinking water contamination incident. Information from these sources can be integrated into a CWS by developing a reliable and automated link between the public health sector and drinking water utilities. • Enhanced security monitoring. Security breaches, witness accounts, and notifications by perpetrators, news media, or law enforcement can be monitored and documented through enhanced security practices. This component has the potential to detect a tampering event in progress, potentially preventing the introduction of a harmful contaminant into the drinking water system.
5 • Routine sampling and analysis. Water samples can be collected at a predetermined frequency and analyzed to establish a baseline of contaminants of concern. This will provide a baseline for comparison during the response to detection of a contamination incident. In addition, this component requires continual testing of the laboratory staff and procedures so that everyone is ready to respond to an actual incident. A CWS is not merely a collection of monitors and equipment placed throughout a water system to alert of intrusion or contamination. Fundamentally, it is information acquisition and management. Different information streams must be captured, managed, analyzed, and interpreted in time to recognize potential contamination incidents and mitigate the impacts. Each of these information streams can independently provide some value in terms of timely initial detection. However, when these streams are integrated and used to evaluate a potential contamination incident, the credibility of the incident can be established more quickly and reliably than if any of the information streams were used alone. The primary purpose of a CWS is to detect contamination incidents, and implementation of a CWS is expected to result in dual-use benefits that will help to ensure its sustainability within a utility. Many utilities are currently implementing some monitoring and surveillance activities, yet these activities are either lacking critical components or have not been integrated in a manner sufficient to meet the primary objectives of a CWS — timely detection of a contamination incident. For example, although many utilities currently track consumer complaint calls, a CWS requires a robust spatially-based system that, when integrated with data from public health surveillance, online water quality monitoring, and enhanced security monitoring, will provide specific, reliable, and timely information for decision makers to establish credibility and respond in an effective manner. Beyond each individual component of the CWS, coordination between the utility, the public health agency, local officials, law enforcement, and emergency responders, among others, is needed to develop an effective consequence management plan that ensures appropriate actions will occur in response to detection by different components. Critical to timely response is an advanced and integrated laboratory infrastructure to support baseline monitoring and analysis of samples collected in response to initial detections. In the absence of a reliable and sustainable CWS, a utility’s ability to respond to contamination incidents in a timely and appropriate manner is limited. Still, the challenge in applying a CWS is to reliably integrate the multiple streams of data in order to decide if a contamination incident has occurred. Sensor Network Design Research and Application The overall goal of a CWS is to detect contamination incidents in time to reduce potential public health and economic consequences. The locations of online sensors can be optimized to help achieve these goals as well as other objectives — for example, minimizing public exposure to contaminants, the spatial extent of contamination, detection time, or costs. These objectives are often at odds with each other, making it difficult to identify a single best sensor network design. In addition, there are many practical constraints and costs faced by water utilities. Consequently, designing a CWS is not a matter of performing a single optimization analysis. Instead, the design process is truly a multi-objective problem that requires informed decision making, using optimization tools to identify possible sensor network designs that work well under different assumptions and for different objectives. Water utilities must weigh the costs and benefits of different designs and understand the significant public health and cost tradeoffs. There has been a large volume of research on techniques for sensor placement in the last several years, including a Battle of the Water Sensor Networks that compared 15 different approaches to this problem (Ostfeld et al. 2008). For a review of the large body of sensor placement research for water security, see Appendix A. Sensor placement strategies can be broadly characterized by the technical approach and the type of computational model used. The following categories reflect important differences in proposed sensor placement strategies: • Expert Opinion: Although expertise with water distribution systems is always needed to design an effective CWS, here we refer to approaches that are solely guided by expert judgment. For example, Berry et al. (2005a) and Trachtman (2006) consider sensor placements developed by experts with significant knowledge of water distribution systems. These experts did not use computational models to carefully analyze network dynamics. Instead, they used their experience to identify locations whose water quality is representative of water throughout the network. • Ranking Methods: A related approach is to use preference information to rank network locations (Bahadur et al. 2003; Ghimire et al. 2006). In this approach, a user provides preference values for the properties of a “desirable’’ sensor location, such as proximity to critical facilities. These preferences can then be used to rank the desirability of sensor locations throughout the network. Further, spatial information can be integrated to ensure good coverage of the network. • Optimization: Sensor placement can be automated with optimization methods that computationally search for a sensor configuration that minimizes contamination risks. Optimization methods use a computational model to estimate the performance of a sensor configuration. For example, a model might compute the expected impact of an ensemble of contamination incidents, given sensors placed at strategic locations. See Appendix A for further discussion on sensor placement optimization literature. This report focuses on the use of optimization to select sensor locations for a CWS. However, designing a CWS is not a matter of performing a single sensor placement analysis;
6 there are many factors that need to be considered when performing sensor placement, including utility response, the relevant design objectives, sensor behavior, practical constraints and costs, and expert knowledge of the water distribution system. In many cases, these factors can be at odds with one another (e.g., competing performance objectives), which makes it difficult to identify a single best sensor network design. The TEVA Research Team has developed a decision-making process for CWS design that is composed of a modeling process and a decision-making process that employs optimization (Murray et al. 2008b). This modeling process includes creating or utilizing an existing network model for hydraulic and water quality analysis, describing sensor characteristics, defining the contamination threats, selecting performance measures, estimating utility response times following detection of contamination incidents, and identifying a set of potential sensor locations. The decision-making process involves applying an optimization method and evaluating sensor placements. The process is informed by analyzing tradeoffs and comparing a series of designs to account for modeling and data uncertainties. The subsequent chapters of this report discuss this process in detail and illustrate sensor placement optimization using the TEVA-SPOT Toolkit (Berry et al. 2008b). The TEVA-SPOT Software The TEVA-SPOT software is an application of the TEVA framework. The software consists of three main software modules that follow the diagram that was shown in Figure 1-1, and more specifically, in Figure 1-2. The first software module simulates the set of incidents in the threat ensemble. The second software module calculates the potential consequences of the contamination incidents contained in the threat ensemble. The third software module optimizes for sensor placement. The software is described in more detail in Chapters 6 and 7 of this report, and briefly summarized here Figure 1-
Drying
Are there ways to flash dry or freeze dry wet materials in a house so that mold does not grow? http://www.barr-rosin.com/products/flash-dryer.asp
Are the chemical or naturally occurring materials that can be used for quick drying?
Team Knowledge Key Project References
http://www.cdc.gov/healthywater/emergency/safe_water/personal.html
http://wi.water.usgs.gov/surface-water/flood2008/
http://towcenter.org/blog/sensor-journalism-and-water-pollution/
http://postscapes.com/mobile-water-sensing-mobosens
http://ready.wi.gov/media/pdf/Flood_Awareness_Week_2010.pdf
Water Contamination Websites
http://en.wikipedia.org/wiki/Ultraviolet_germicidal_irradiation
http://www.pelicanwater.com/uv_disinfection_systems.php?gclid=CIyeiYzslroCFckRMwodIScApA
http://smwi.org/wp-content/uploads/2013/06/WaterDeptSMS13.pdf
http://smwi.org/wp-content/uploads/2011/11/Stormwater-Fact-Sheet.pdf
http://www.cdc.gov/healthywater/emergency/safe_water/index.html
http://www.cdc.gov/healthywater/
http://www.cdc.gov/nors/resources.html
http://www.ask.com/wiki/1993_Milwaukee_Cryptosporidiosis_outbreak
http://phc.amedd.army.mil/PHC%20Resource%20Library/DrinkingWaterContamination.pdf
Table 1-1. How TEVA supports the six basic vulnerability assessment elements.
VA Basic Element
TEVA Element
Characterize water system
Simulation of Incidents (development of EPANET network model)
Identify and prioritize adverse impacts
Simulation of Incidents
Consequence Assessment
Identify critical assets
Consequence Assessment
Assess likelihood of adverse impacts
Simulation of Incidents
Evaluation of existing countermeasures
Threat Mitigation Analysis
Develop risk reduction plan or actions
Threat Mitigation Analysis
Without specific intelligence information, one cannot predict exactly how terrorist groups might sabotage a water system. Therefore, the TEVA framework is based on a probabilistic analysis of a large number of likely contamination incidents. Although the number of possible variations on terrorist attacks is nearly infinite, by selecting a “large enough” set of likely incidents, the expected impacts of contamination incidents can be assessed. A single contamination incident can be defined by the type of contaminant, the amount and concentration of the contaminant, the location of the injection into the distribution system, and the start and stop time of the injection. A threat ensemble, then, is a large collection of distinct incidents. In the TEVA framework (as well as in previous work by Ostfeld et al. 2004), the vulnerability of a water system is based on an assessment of the entire threat ensemble. TEVA fits into the general VA structure as shown in Table 1-1.
Drinking Water Contamination Warning Systems
Research on methods to mitigate the impacts of contamination incidents have converged over the last
several years on the concept of a contamination warning system (CWS).
CWSs have been proposed as a promising approach for the early detection and management of contamination incidents in drinking water distribution systems (ASCE 2004; AWWA 2005; U.S. EPA 2005a). EPA is piloting CWSs through the Office of Water’s Water Security (WS) Initiative, formerly called WaterSentinel, at a series of drinking water utilities.
The key to an effective response to a water contamination incident is minimizing the time between detection of a contamination incident and implementation of effective response actions that mitigate further consequences. Implementation of a robust CWS can achieve this objective by providing an earlier indication of a potential contamination incident than would be possible in the absence of a CWS. A CWS is a proactive approach that uses advanced monitoring technologies and enhanced surveillance activities to collect, integrate, analyze, and communicate information that provides a timely warning of potential contamination incidents.
The WS Initiative promotes a comprehensive CWS that is theoretically capable of detecting a wide range of contaminants, covering a large spatial area of the distribution
system, and providing early detection in time to mitigate impacts (U.S. EPA 2005c). Components of the WS Initiative include:
• Online water quality monitoring. Continuous online monitors for water quality parameters, such as chlorine residual, total organic carbon, electrical conductivity, pH, temperature, oxidation reduction potential, and turbidity help to establish expected baselines for these parameters in a given distribution system. Event detection systems, such as CANARY (Hart et al. 2007), can be used to detect anomalous changes from the baseline to provide an indication of potential contamination. Other monitoring technologies can be used as well, such as contaminant-specific monitors, although the goal is to detect a wide range of possible contaminants.
• Consumer complaint surveillance. Consumer complaints regarding unusual taste, odor, or appearance of the water are often reported to water utilities, which track the reports as well as steps taken by the utility to address these water quality problems. The WS Initiative is developing a process to automate the compilation and tracking of information provided by consumers. Unusual trends that might be indicative of a contamination incident can be rapidly identified using this approach.
• Public health surveillance. Syndromic surveillance conducted by the public health sector, including information such as unusual trends in over-the-counter sales of medication, as well as reports from emergency medical service logs, 911 call centers, and poison control hotlines might serve as a warning of a potential drinking water contamination incident. Information from these sources can be integrated into a CWS by developing a reliable and automated link between the public health sector and drinking water utilities.
• Enhanced security monitoring. Security breaches, witness accounts, and notifications by perpetrators, news media, or law enforcement can be monitored and documented through enhanced security practices. This component has the potential to detect a tampering event in progress, potentially preventing the introduction of a harmful contaminant into the drinking water system.
5
• Routine sampling and analysis. Water samples can be collected at a predetermined frequency and analyzed to establish a baseline of contaminants of concern. This will provide a baseline for comparison during the response to detection of a contamination incident. In addition, this component requires continual testing of the laboratory staff and procedures so that everyone is ready to respond to an actual incident.
A CWS is not merely a collection of monitors and equipment placed throughout a water system to alert of intrusion or contamination. Fundamentally, it is information acquisition and management. Different information streams must be captured, managed, analyzed, and interpreted in time to recognize potential contamination incidents and mitigate the impacts. Each of these information streams can independently provide some value in terms of timely initial detection. However, when these streams are integrated and used to evaluate a potential contamination incident, the credibility of the incident can be established more quickly and reliably than if any of the information streams were used alone. The primary purpose of a CWS is to detect contamination incidents, and implementation of a CWS is expected to result in dual-use benefits that will help to ensure its sustainability within a utility.
Many utilities are currently implementing some monitoring and surveillance activities, yet these activities are either lacking critical components or have not been integrated in a manner sufficient to meet the primary objectives of a CWS — timely detection of a contamination incident. For example, although many utilities currently track consumer complaint calls, a CWS requires a robust spatially-based system that, when integrated with data from public health surveillance, online water quality monitoring, and enhanced security monitoring, will provide specific, reliable, and timely information for decision makers to establish credibility and respond in an effective manner. Beyond each individual component of the CWS, coordination between the utility, the public health agency, local officials, law enforcement, and emergency responders, among others, is needed to develop an effective consequence management plan that ensures appropriate actions will occur in response to detection by different components. Critical to timely response is an advanced and integrated laboratory infrastructure to support baseline monitoring and analysis of samples collected in response to initial detections. In the absence of a reliable and sustainable CWS, a utility’s ability to respond to contamination incidents in a timely and appropriate manner is limited. Still, the challenge in applying a CWS is to reliably integrate the multiple streams of data in order to decide if a contamination incident has occurred.
Sensor Network Design Research and Application
The overall goal of a CWS is to detect contamination incidents in time to reduce potential public health and economic consequences. The locations of online sensors can be optimized to help achieve these goals as well as other objectives — for example, minimizing public exposure to contaminants, the spatial extent of contamination, detection time, or costs. These objectives are often at odds with each other, making it difficult to identify a single best sensor network design. In addition, there are many practical constraints and costs faced by water utilities. Consequently, designing a CWS is not a matter of performing a single optimization analysis. Instead, the design process is truly a multi-objective problem that requires informed decision making, using optimization tools to identify possible sensor network designs that work well under different assumptions and for different objectives. Water utilities must weigh the costs and benefits of different designs and understand the significant public health and cost tradeoffs.
There has been a large volume of research on techniques for sensor placement in the last several years, including a Battle of the Water Sensor Networks that compared 15 different approaches to this problem (Ostfeld et al. 2008). For a review of the large body of sensor placement research for water security, see Appendix A. Sensor placement strategies can be broadly characterized by the technical approach and the type of computational model used. The following categories reflect important differences in proposed sensor placement strategies:
• Expert Opinion: Although expertise with water distribution systems is always needed to design an effective CWS, here we refer to approaches that are solely guided by expert judgment. For example, Berry et al. (2005a) and Trachtman (2006) consider sensor placements developed by experts with significant knowledge of water distribution systems. These experts did not use computational models to carefully analyze network dynamics. Instead, they used their experience to identify locations whose water quality is representative of water throughout the network.
• Ranking Methods: A related approach is to use preference information to rank network locations (Bahadur et al. 2003; Ghimire et al. 2006). In this approach, a user provides preference values for the properties of a “desirable’’ sensor location, such as proximity to critical facilities. These preferences can then be used to rank the desirability of sensor locations throughout the network. Further, spatial information can be integrated to ensure good coverage of the network.
• Optimization: Sensor placement can be automated with optimization methods that computationally search for a sensor configuration that minimizes contamination risks. Optimization methods use a computational model to estimate the performance of a sensor configuration. For example, a model might compute the expected impact of an ensemble of contamination incidents, given sensors placed at strategic locations. See Appendix A for further discussion on sensor placement optimization literature.
This report focuses on the use of optimization to select sensor locations for a CWS. However, designing a CWS is not a matter of performing a single sensor placement analysis;
6
there are many factors that need to be considered when performing sensor placement, including utility response, the relevant design objectives, sensor behavior, practical constraints and costs, and expert knowledge of the water distribution system. In many cases, these factors can be at odds with one another (e.g., competing performance objectives), which makes it difficult to identify a single best sensor network design.
The TEVA Research Team has developed a decision-making process for CWS design that is composed of a modeling process and a decision-making process that employs optimization (Murray et al. 2008b). This modeling process includes creating or utilizing an existing network model for hydraulic and water quality analysis, describing sensor characteristics, defining the contamination threats, selecting performance measures, estimating utility response times following detection of contamination incidents, and identifying a set of potential sensor locations. The decision-making process involves applying an optimization method and evaluating sensor placements. The process is informed by analyzing tradeoffs and comparing a series of designs to account for modeling and data uncertainties. The subsequent chapters of this report discuss this process in detail and illustrate sensor placement optimization using the TEVA-SPOT Toolkit (Berry et al. 2008b).
The TEVA-SPOT Software
The TEVA-SPOT software is an application of the TEVA framework. The software consists of three main software modules that follow the diagram that was shown in Figure 1-1, and more specifically, in Figure 1-2. The first software module simulates the set of incidents in the threat ensemble. The second software module calculates the potential consequences of the contamination incidents contained in the threat ensemble. The third software module optimizes for sensor placement. The software is described in more detail in Chapters 6 and 7 of this report, and briefly summarized here
Figure 1-
Helpful Hints
I thought this one had helpful hints - http://fllshelton.shsrobotics.org/html/design_a_robot.htm
This one, too -http://www.firstlegoleague.org/sites/default/files/Challenge/TeamResources/NaturesFury/2013RobotDesign.pdf.
Team Building Ideas - http://www.techbrick.com/Lego/TechBrick/Resources/Groups/index.html
Flood Rescue
WEB References on Flood Issues: Rescue/ Emergency Response/ Rescue Techniques/ Preparedness/ Training/ Equipmenthttps://sites.google.com/site/sarbook1/water-flood-response
http://www.jumpjet.info/Emergency-Preparedness/Neighborly-Response/Outside/Flood_Rescue.pdf
http://en.wikipedia.org/wiki/Swiftwater_rescue
http://www.wsoctv.com/news/news/local/storm-water-services-workers-learn-how-rescue-them/nYjzk/
http://www.rocknrescue.com/acatalog/Water-Rescue-Equipment.html?gclid=CLyZrofW87kCFbFDMgoddlwAyA
http://www.angelfire.com/tx/rti/pagefast.html
http://gcc.glendale.edu/fire/Documents/ClassMaterials/River&Flood/VFARiverOP.pdf
http://www.boatstogo.com/images/inflatable-boats-SD430/Enid-firefighters-hon.pdf
http://charmeck.org/mecklenburg/county/mediaroom/pr/pages/charlotte-mecklenburg-storm-water-services-partners-with-charlotte-fire-department-for-swift-water-training.aspx
http://www.morganton.com/news/article_58373448-0b31-11e3-921c-0019bb30f31a.html
http://www.redcross.org/prepare/disaster/flood
http://phys.org/news/2013-09-satellite-crisis-teams-internet.html
Mold
Information about mold and its heath consequences
http://www.mnn.com/family/protection-safety/stories/5-unsavory-side-effects-of-flooding#
http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5508a1.htm
http://www.epa.gov/iedmold1/moldresources.html
http://www.health.state.mn.us/divs/eh/emergency/natural/floods/mold/mold.html
http://blackmold.awardspace.com/mold-flooding.html
www.lsuagcenter.com/NR/.../Pub2949B**Mold**RemovalFINAL1.pdf
https://www.vdh.virginia.gov/Epidemiology/DEE/publichealthtoxicology/documents/pdf/moldQ&A.PDF
http://www.safeenvironments.com/floodmold.htm
What are ways to prevent or kill mold?
Drying
Are there ways to flash dry or freeze dry wet materials in a house so that mold does not grow?
http://www.barr-rosin.com/products/flash-dryer.asp
Are the chemical or naturally occurring materials that can be used for quick drying?
Products That Remove Mold
http://blackmold.awardspace.com/kill-remove-mold.html
Are there biocides other than bleach that could be used?
http://www.epa.gov/iedmold1/moldguide.html
http://www.ehow.com/list_6912958_list-biocide-mold-removers.html
Removing Mold from Food
http://www.ehow.com/how_5558802_kill-mold-foods.html
Heating and Freezing is used for Killing Bedbugs. Could it be useful for killing mold?
http://www.bell-environmental.com/heat_treatment_vs_freezing_bed_bugs.aspx
Waterborne Illness
What waterborne illnesses are associated with flooding?http://www.cdc.gov/healthywater/emergency/flood/standing.html
http://energyskeptic.com/2012/floods-and-water-borne-disease/
http://business.inquirer.net/76097/beware-of-flood-borne-diseases
http://www.lenntech.com/library/diseases/diseases/waterborne-diseases.htm
Is Drinking Water Safe After Flooding? If Not, How Do You Make it Safe?
http://blogs.denverpost.com/food/2013/09/16/tips-to-ensure-safe-drinking-water-in-the-wake-of-colorado-flooding/21487/
Municipal Water
http://www.denverpost.com/news/ci_24093109/floods-stressing-water-treatment-issues-could-still-loom
http://www.ct.gov/dph/cwp/view.asp?a=4186&q=513124
Well Water
http://www.health.ny.gov/environmental/water/drinking/flooding/docs/private_wells.pdf
http://www.shl.uiowa.edu/news/wellflood.xml
How do countries without regular safe sources of drinking water get clean drinking water? Could what they do be of use in flooded areas?
http://www.denverpost.com/news/ci_24093109/floods-stressing-water-treatment-issues-could-still-loom
How are waterborne illnesses treated?
Sewage
http://www.mnn.com/family/protection-safety/stories/5-unsavory-side-effects-of-flooding#http://www.monroecounty.gov/des-basement_sewage.php
http://www.ehow.com/info_8079515_physical-drinking-water-sewage-systems.html
http://www.health.gov.sk.ca/flood-cleanup
http://healthvermont.gov/news/2011/060111_flood_contaminated_water.aspx
http://www.health.state.mn.us/divs/eh/emergency/natural/floods/sewage/sewage.html
http://www.nyc.gov/html/dep/pdf/**flooding**_**clean**-**up**.pdf
Critters
MosquitosSnakes
Frogs
Rats