Perspectives on Emerging Monitoring Technologies: Understanding Factors that Affect Adoption

There are numerous technological acceptance and adoption theories that seek to explain how, why, and at what rate new technologies diffuse through systems over time. While the models can be used to explain why users adopt technologies, they do so in a general way and few, if any, studies have addressed the factors that affect monitoring technology adoption in coastal management. This study explores coastal managers’ and water quality monitors’ perspectives on water quality monitoring technology using various technology acceptance and adoption theories as a theoretical framework to better understand the factors that affect water quality monitoring technology adoption in coastal management. This study utilizes qualitative and quantitative methods of data collection in a two-part approach: (1) semi-structured interviews, and (2) online surveys. In person interviews were conducted with RI coastal managers to get an in-depth understanding of the factors that affect technology adoption, attitudes and perceptions of technology innovations, and technological needs based on environmental conditions. Data from the interviews were used, along with other sources, to develop a framework of factors affecting water quality technology adoption in coastal management. The online survey investigated how the framework applies to coastal researchers within the National Estuarine Research Reserve System (NERRS). In addition, the survey investigated respondents’ likelihood of adopting two innovative monitoring technologies: a low-cost, handheld nanoscale biosensor and an Imaging FlowCytobot. Factors from the existing literature on technology adoption, such as technological conditions and external conditions, and additional factors, such as accuracy, reliability, and approved method for water quality monitoring, greatly influence the rate of technology adoption in coastal management. Findings from this study show that characteristics, needs, and preferences of coastal managers greatly affect which factors are important for technology adoption and that these factors do not necessarily align with the literature. In addition, a majority of respondents was willing to adopt the nanoscale biosensor. Observability, the degree to which the benefits (or limitations) of an innovation are visible to others, was statistically significantly more important to respondents who were not willing to adopt the biosensor than those who were willing to adopt it. Findings from this study provide a more detailed understanding of perceptions and attitudes towards existing and emerging monitoring technology; identify potential developments for technological innovations that can be used to better address changing environmental conditions; and provide coastal managers/water quality program directors with insight into how individuals are using technology in order to develop better water quality monitoring programs.


INTRODUCTION
Coastal environments are directly and indirectly affected by natural processes and anthropogenic impacts, such as oil spills, land runoff, pipe discharges, nutrient loading, harmful algal blooms (HABs), climate change, sea level rise, and human population growth (Burroughs 2011). Changing environmental conditions are receiving increased attention from coastal managers and researchers (Betsill and Bulkeley 2007; "What is a Harmful Algal Bloom" 2016). The pace at which the coastal environment is changing requires coastal management and monitoring capabilities to evolve quickly in order to effectively quantify the change. Coastal managers and individuals responsible for water quality monitoring must adapt to deal with the rapid evolution of technology. Therefore, it is important to understand how coastal managers incorporate new technology into their water quality monitoring programs.
There are numerous general technological acceptance and adoption theories and models that seek to explain how, why, and at what rate new ideas and technologies spread or diffuse through human social systems over time (e.g., Crann et al. 2015;Rice and Pearce 2015;Rice 2009;Rogers 2003). Such theories propose numerous factors (or predictors) that influence a user's decision to adopt new technology and therefore, help explain why certain technologies have different rates of adoption (Rogers 2003). Factors that affect the adoption of technology vary depending on the needs of the user population (Renaud and van Biljon 2008). Few, if any, studies have applied these theories of technology adoption to water quality monitoring in the coastal zone.
Using technology adoption and acceptance theories as a conceptual framework, this research explores methods and technologies currently used by individuals involved in coastal water quality monitoring, trends in water quality monitoring technology, and coastal managers' perspectives on emerging monitoring technology, in order to better understand how different factors affect water quality monitoring technology adoption specific to the field of coastal management.

Technology Adoption and Acceptance Theories
A number of theories describe the general adoption and acceptance processes of technological innovations. An innovation can be defined as "an idea, practice, or object that is perceived as new by an individual or other unit of adopting" (Rogers 2003, p. 12). This study defines an innovation similarly to Rogers' definition but focuses on emerging, water quality monitoring technologies that are perceived as new by an individual or group associated with coastal or marine environments.

Diffusion of Innovations Theory (DIT), Unified Theory of Acceptance and Use of
Technology (UTAUT), and Technology Acceptance Model (TAM) explain technological, individual, and organizational factors and processes that affect adoption and acceptance of information technologies. These theories propose how and why innovations are adopted and accepted, yet there are limitations to each of them and no one theory is universally accepted (Kiwanuka 2015).  (Crann et al., 2015;Davis, 1989;Rice and Pearce, 2015;Rogers, 2003).  (Rogers 2003). Compatibility refers to the degree to which an innovation is perceived as being consistent with existing values, experiences, and needs of potential adopters (Rogers 2003). Complexity refers to the degree to which the innovation is perceived as difficult to use and understand (Rogers 2003). Complexity has been found to be negatively related to adoption in that innovations of exceeding complexity are less likely to be adopted (Crann et al. 2015). Trialability refers to the opportunity potential adopters have to experiment with the innovation for a limited time prior to adoption. Rogers (2003) states that an innovation that can be tested is likely to reduce the uncertainty potential users have when considering it for adoption (Rogers 2003). Observability is the degree to which the outcomes of the innovation are visible to others. The more obvious it is for individuals to see how the technology benefits others who are using it, the more likely they are to adopt it (Rogers 2003 (Crann et al. 2015). Perceived ease of use is the degree to which a person believes that using a particular technology would be free of effort.

Predictor Category Predictors of Technology Adoption
Perceived usefulness is the extent to which a person believes that the technology will enhance his/her job performance (Crann et al. 2015;Davis 1989).
The Unified Theory of Acceptance and Technology Use (UTAUT), developed by Venkatesh et al. (2003) builds upon TAM by seeking to explain user intentions and behaviors. UTAUT proposes four key constructs: performance expectancy, effort expectancy, social influence and facilitating conditions (Venkatesh et al. 2003).
Facilitating conditions refer to the degree to which an individual believes the organization and technological infrastructure can support a particular innovation (Venkatesh et al. 2003 where the individual is situated (e.g., coastal management agency). Some predictors fit into more than one category. This study applies this framework within the context of coastal water quality monitoring.

Water Quality Monitoring
Surface and ground water quality are influenced by anthropogenic impacts and natural processes. Surface water quality is directly related to atmospheric pollution, effluent discharges, water resource exploitation, and the use of agricultural chemicals (Glasgow et al. 2004). Typical water quality monitoring programs assess water quality by monitoring a suite of physical, chemical, and biological parameters, including: pH, dissolved oxygen, nutrients, chlorophyll a, fecal matter, contaminants, metals, polychlorinated biphenyls (PCBs) in fish tissue, infaunal benthic macroinvertebrate communities, amphipods, phytoplankton assemblages, and many more (Stoermer 1978;USEPA 2009).
Advancements in water quality monitoring technology are continuously emerging. Historically, water quality monitoring techniques have utilized costly, timeand labor-intensive on-site sampling and have been limited on temporal and spatial scales (Glasgow et al. 2004). In order to effectively manage and preserve water resources, accurate, intensive, and long-term data collection needs to occur. In the last several years, there has been an increased interest in the development of molecular, optical, biosensor, and analytical detectors for assessment of toxins, contaminants, and biological components in marine, estuarine, and freshwater systems (Glasgow et al. 2004). Innovative technologies such as lab on a chip technologies (e.g., spectroscopic nanoscale biosensors and environmental sample processors), visualization technologies (e.g., imaging flow cytometry), molecular probes, time series sensors, near real-time detection systems, photothermal sensors, and environment sensor networks are being developed in order to address changing environmental conditions (Dashkova et al. 2016;de Freitas et al. 2009;Glasgow et al. 2004;Heisler et al. 2008;Schaap 2012;Zheng et al. 2016 HAB events negatively affect the economy of coastal communities through costs associated with beach cleanups, fishery closures, decreased tourism, and loss of wages. Additionally, the shellfish industry suffers from loss of revenue due to mandated temporary closures of shellfish beds and prevention of harvesting and selling goods. Van Dolah et al. (2001) reported that HABs are responsible for the loss of millions of dollars.

Water Quality and Harmful Algal Blooms
In the US, there are increasing concerns associated with water quality impacts of HABs ("Harmful Algal Blooms, Tiny Plants with a Toxic Punch" 2017). A HAB event occurs when "colonies of algae…grow out of control and produce toxic or harmful effects on people, fish, shellfish, marine mammals and birds" ("What is a Harmful Algal Bloom?" n.d.). There are two different types of HABs: (1) those that involve toxins or harmful metabolites; and (2) those that are nontoxic. Both forms of HABs result in harmful impacts to the marine and human environments from either their direct production of toxins or through changes to the ecosystem structure and dynamics due to their accumulating biomass (Anderson et al. 2002;Hoagland et al. 2002). Examples of harmful effects of HABs include human illness from toxic seafood consumption or toxin exposure, mass death of marine mammals and birds, and changes within marine ecosystems (Anderson et al. 2002). Over the last 20 years, HABs have increased in frequency, duration, geographic extent, number of toxic species, number of fisheries effects, and costs (Heisler et al. 2008).

Coastal Water Quality Management in the US and Rhode Island
Effective water quality monitoring is critical for water resource management programs (Glasgow et al. 2004

Research Objectives
Few, if any, studies have been conducted to understand the factors that affect monitoring technology adoption in coastal management. This study investigates how and why water quality monitoring technology is adopted in coastal management and the factors that drive technology adoption and acceptance. In particular, the study: (1) highlights technologies currently being used by coastal managers (and other individuals involved in monitoring coastal waters) in Rhode Island and in the NERRS sites; (2) investigates how individual, organizational, and technological factors influence the adoption of water quality monitoring technology in RI; (3) identifies the most important factors influencing adoption of water quality monitoring technology among NERRS staff; and (4) explores the potential adoption of an emerging technology for monitoring HABs.

METHODS
This study utilized qualitative and quantitative research methods in order to better understand the adoption and use of water quality monitoring technology in coastal management. A two-part approach was used: (1) semi-structured interviews with 12 coastal managers and others involved in water quality monitoring in RI; and (2) online surveys of 26 research staff members at the National Estuarine Research interview focused on the current water quality monitoring technologies used by the respondent so they could draw upon firsthand experience, rather than on a hypothetical situation (Weiss 1994).
A combination of purposive and snowball sampling was used to identify potential interview participants (Robson 2011 were asked for names of other potential interview respondents, as part of the snowball sample approach. Snowball sampling continued until the point of data saturation, which is the point at which no new information is observed in the data.
Saturation has been found to occur in qualitative studies with as few as 6 to 12 interview participants, with more respondents needed when they are not a homogenous group, data quality is poor, or the topic is broad (Guest et al. 2011).

Data Analysis of Interviews
All interviews were recorded and transcribed. Interview transcriptions were coded using NVivo 11 software. Thematic analysis, which is a method for identifying, analyzing, and reporting patterns within the data, was used to minimally organize and describe the data through the development of themes and subthemes and finally by relating themes to theoretical models of technology adoption (Braun and Clark 2006;Crann et al. 2015;Ryan and Bernard 2003). An initial set of codes, (called "nodes" in NVivo), based on the framework and other questions in the interview instrument, were created prior to coding interview data. Subsequent codes emerged throughout the coding process, for a total of 49 individual codes.

Structured surveys of NERRS staff 3.2.1 Data Collection
The survey next investigated how the framework of factors developed in phase 1 applies within a particular coastal management context Survey participants were initially contacted through an e-mail, which included a link to the survey. Reminder emails were used to increase survey response rate (Dillman et al. 2014). The survey was distributed through an online survey platform (Survey Monkey) during October 2016 to January 2017, and was designed to take between 15 and 20 minutes to complete.

Data Analysis of surveys
The online surveys provided quantitative data related to the framework on coastal water quality monitoring technology adoption, how and why new monitoring technology is adopted in coastal management, and the importance of framework factors to NERRS staff. The average rating for each factor across all respondents was calculated. Data were statistically analyzed using descriptive statistics and predictive analyses (e.g., Mann-Whitney U test). The Mann-Whitney U test was used to compare mean factor scores between respondents who were willing to adopt the nanoscale biosensor, an emerging tool for measuring presence and abundance of HABs in coastal waters, and respondents who were not willing to adopt the nanoscale biosensor. Significance for all statistical tests was determined at the commonly accepted 5% level. According to another respondent, who is actively involved in the DOH's Beach Programs, the number of beach closures is the lowest it has been in 37 years.  (13), 38% of the respondents identified as female (10) and 11% chose not to respond

National Estuarine Research Reserve System Survey --Sample Characteristics
(3). All respondents had some level of higher education, with ten respondents having doctorate degrees, seven respondents having graduate degrees, and nine respondents having bachelors' degrees. Job titles of respondents are outlined in Table 2. Respondents held their current positions for an average of seven years. Respondents were actively involved in collecting water quality data or managing a water quality monitoring program, with the highest number of respondents working on water quality monitoring-related issues between fifty and seventy-five percent of their time (Figure 4). Appendix C lists the typical water quality variables monitored by survey respondents.
The most common water quality monitoring technology used by survey respondents was the YSI Multiparameter Sonde (various models including: 6920, 6820, EXO 1 and 2). Twenty-one survey respondents cited various models of the YSI Multiparameter Sonde as the most commonly used instrument for measuring water quality parameters. The YSI measures physical and chemical parameters of the water, such as temperature, pH, dissolved oxygen, salinity, and others depending on the model used. Twenty-three other instruments were noted by survey respondents as currently being used for water quality monitoring within the NERRS (Appendix D).  Perceptions of current water quality conditions varied across the survey respondents, ranging from severely degraded to pristine, with the highest number of respondents classifying water quality at their respective reserves as average.
However, due to the large area comprising some reserves (e.g., Kachemak NERR in Alaska encompasses 372,000 acres), it was difficult for respondents to make a judgement of the overall water quality status within the entire reserve. One respondent highlighted the difficulty of making a general statement regarding water quality status in the following comment: Respondents were asked to characterize themselves as one of Rogers' (2003) adopter categories based on a description that best fit their individual willingness to adopt new technologies (Table 3). Respondents characterized themselves as either early adopters, early majority, or late majority (Table 3). No respondents considered themselves an innovator or a laggard when adopting new technology.  Additionally, one respondent reported that accuracy and precision of the technology was one of the most influential factors when deciding to adopt a new monitoring technology.
Respondents indicated that durability of the monitoring instrument is an important factor in decisions to adopt new technology for coastal monitoring.
Durability refers to the degree that the physical technology is considered durable and/or able to withstand difficult environmental conditions. One respondent mentioned the value of automated maintenance features, such as self-cleaning wipers on a YSI Multiparameter Sonde and self-cleaning brushes on probes, to ensure the device continues to function in a dynamic environment. One respondent stated that the durability, or ruggedness, of the instrument was one of the most important reasons for adopting a specific monitoring instrument.
Almost all respondents found that cost associated with monitoring equipment was a major influence on their decisions to adopt new monitoring technology. In fact, four respondents cited cost of the technology as the most important factor when deciding to adopt a new monitoring technology. Cost refers to the cost associated with adopting and maintaining the instrument. Cost is related to external condition, in that the agency/organization must have sufficient resources to cover the cost of the technology in order for the technology to be adopted. Most respondents, including those from state and volunteer-based organizations, stated that external conditions, such as the resources available for acquisition of new monitoring technologies, were extremely limited, and therefore, the cost of the technology itself was an important factor influencing their decision to adopt a new technology.
Respondents also highlighted that the costs associated with data collection, analysis, and maintenance, not just the initial costs of the tool, influence their decisions to choose certain technologies.

Individual factors affecting monitoring technology adoption in coastal management
Perceived usefulness, the degree to which the technology enhanced an individual's job performance and ability to collect high-quality data, was the most influential factor in the individual category regarding a respondent's decision to Another respondent commented on the importance of creating consistency across projects, within and outside of their organizations. One other respondent noted that he adopted a specific water quality monitoring technology because, [i]t is also a standard piece of equipment used by the Narragansett Bay National Estuarine Research Reserve, so they have equipment specifications that they're using…we just stuck to those.
Three respondents indicated that the availability of technological support is an important factor when deciding to adopt new technology. Two respondents cited technological support as the most important factor when deciding to adopt a new monitoring technology, with one respondent stating that customer service was one of the most important reasons for adopting a specific technology. Water quality monitoring instruments are typically expensive, and in some cases, complex, so respondents found it beneficial to have responsive technological support for unanticipated issues, as one respondent stated: If we had an issue they [instrument support team] would say to just drive down here tomorrow and we will take a look at it, which you can't get from any of these technical companies. So, that was the kind of service you had…that was a big deal, to have that support very close by where the next day you can resolve an issue.
Several respondents discussed the need for technology to be an approved method for water quality monitoring, which refers to whether the technology is an approved method by the regulating and/or funding agency for water quality monitoring.
Organizations in which the water quality data is intended to support legal defenses or to support management decisions consider whether the new technology is an approved method for water quality monitoring. One respondent emphasized the importance of using only approved methods for water quality monitoring stating, [t]he method is approved by the EPA, which is who we have to validate all of our data through--with a quality assurance project plan--so the data can be used by EPA and others. Most of our funding comes from that source, so that's very important to us.
According to one source, "there's a list and we can only use things on the list. If it is outside of the list, it has to be vetted to be included."    When asked to rank the factors, six respondents cited technological conditions as the most important factor influencing their decision to adopt new monitoring technology. Four respondents cited relative advantage, reliability, and accuracy as the most important factor (Figure 6).

Scenarios
Survey respondents were asked to state their likeliness to adopt two very different technologies: (1) a low-cost, handheld nanoscale biosensor that can be used in the field to detect the concentration of specific algae species or other biological components present in a small water sample; and (2) a high-resolution, continuous automated underwater microscope (Imaging FlowCytobot) that can be used to rapidly detect the presence of algae species by analyzing how the cells fluoresce or scatter light. Thirteen respondents stated they were likely to adopt the nanoscale biosensor, while only one respondent stated he was likely to adopt the Imaging FlowCytobot (Table 6). Table 6. Likeliness of adopting water quality monitoring technology used in survey scenarios.

Imaging FlowCytobot (Number of respondents)
Extremely Likely 3 0 Likely 10 1 Neutral 7 9 Unlikely 5 10 Extremely Unlikely 1 6 Survey respondents were more likely to adopt the nanoscale biosensor than the Imaging FlowCytobot. Seven respondents stated that they do not have a need for the nanoscale biosensor and three respondents stated they do not have the financial resources to buy an instrument like this. Respondents also mentioned that adoption of the nanoscale biosensor is dependent on characteristics such as reliability, affordability, and the "the instrument's performance relative to other instruments that are available." Eight respondents stated they do not have the financial resources to purchase the FlowCytobot and eight respondents stated they do not have a need for an instrument like this as their reasons for low likelihood of adoption. Two respondents already adopted the Imaging FlowCytobot at their respective reserves. Three respondents stated that cost was the limiting factor when adopting an instrument like the Imaging FlowCytobot. In both scenarios, a couple of respondents mentioned that in order to adopt a new technology, the NERRS or collaborating researchers must be conducting the type of research that requires this type of technology. Additionally, in response to adopting new technology used to monitor water quality, three respondents noted that they are required to follow NERRS standard operating procedures or acquire approval from management, and therefore, are limited in terms of the instruments they are permitted to use.
Observability was the only factor that was statistically significantly different between respondents not willing to adopt the nanoscale biosensor and those who were willing to adopt it (U=9.00, n1=4, n2=13, and p=0.034) (Figure 7). Respondents who were not willing to adopt the nanoscale biosensor rated observability higher than those who were willing to adopt the nanoscale biosensor, when comparing mean ranks. * Figure 7. Mean rankings of factors affecting monitoring technology in coastal management. Observability was statistically significant (p<0.05) and is denoted with an * (U=9.00, n1=4, n2=13, and p=0.034)

Overview
Factors within the technological and organizational categories were found to be most influential for successful adoption of water quality monitoring technology in coastal management. Technological conditions, accuracy, reliability, external organizational conditions, and approved method for water quality monitoring were important factors for both RI coastal managers and NERR researchers. Factors influencing coastal managers' and water quality monitors' decision to adopt new technology seem to be specific to this user group, which is not surprising as coastal managers have certain needs, experiences, and preferences. Key findings from the study suggest,  The most influential factors in an individual's decision to adopt new monitoring technology are related to technological and organizational conditions.  Factors deemed important by coastal managers and water quality monitors do not necessary align with other studies on technology adoption.  There is limited diversity in technologies used for water quality monitoring.  Technology developers and water quality monitoring program directors can utilize findings from this study to develop more applicable and targeted technology and water quality monitoring programs. were cited by interview respondents as important in their decisions to adopt new water quality monitoring technologies. Interview respondents even described several of these additional factors as the most important factors when deciding to adopt new technology. In order to accurately explain why certain water quality monitoring technologies get adopted by individuals involved in coastal water quality monitoring, the framework of factors affecting decisions to adopt would need to be expanded (Table 7). Table 7. Expanded framework of predictors of successful technology adoption in coastal management (based on Crann et al. 2015;Davis 1989;Rice and Pearce 2015;Rogers 2003;interviews in this study). Factors that emerged out of the interviews but were not found in the literature are denoted by asterisk.

Predictor Category Predictors of Technology Adoption
Technological When testing the expanded framework within the NERRS context, some factors were more difficult to measure than others. For instance, some factors represent personal qualities, such as a respondent's values and experiences, while others were external to a respondent, such as resources provided by the organization. Perceived usefulness was one of the factors that was more difficult to assess because it represents an individual's attitude toward a tool that does not tend to be explicitly discussed. During the interviews, when discussing technology that is currently being used for monitoring, it is assumed that the technology indeed helps the researcher perform his/her job; however, determining the degree to which perceived usefulness informed their decision making process was difficult to assess.
Compatibility was also difficult to assess as it refers to the values and experiences of the potential technology user. Understanding the core values of the respondent came out when discussing what she/he perceived to be the most influential factors when deciding to adopt a new monitoring technology, but it was more difficult to parse out how the values of the user affect the adoption of a specific monitoring technology.

Most Influential Factors
The most influential factors for both RI coastal managers and NERRS researchers were related to the technological capabilities of the technology and organizational conditions of the organization/agency in which the individual works.
Across the interviews and surveys, similar factors emerged as being most influential in an individual's decision to adopt a new monitoring technology, which suggests there are similarities among the needs and preferences of RI coastal managers and NERRS staff included in this study.

Technological Category
Several factors within the technological category were cited as the most important factor(s) when deciding to adopt a new monitoring technology. For some respondents, observability was considered an important factor, although it was not the most important factor. As the NERRS survey findings demonstrated, respondents who were not willing to adopt the nanoscale biosensor rated observability higher than respondents who were willing to adopt the nanoscale biosensor. This indicates that respondents who prefer to see the benefits of a device before adopting it were not willing to adopt the biosensor, suggesting that these respondents did not necessarily see any benefits of using the biosensor. If developers of the nanoscale biosensor want to increase the likelihood of adoption, they might consider ways to clearly demonstrate the benefits of this tool to potential users. Complexity might be a barrier to successful technology adoption in these types of coastal monitoring programs. Individuals who invest in the development of new water quality monitoring technologies will need to consider the needs, experiences, and preferences of the individuals that will be using the technology.

Appendices
Appendix A: Interview protocol The following protocol serves as a guide for the interviews. Interviews should be hour-long conservations between the interviewer and the participant.

I. Opening
Describe the general purpose of the research study and the role of the participants.
The purpose of this study is to explore what individuals involved in water monitoring think about technologies in coastal management. This project focuses on how and why certain technologies are used in coastal management.
Explain confidentiality and get consent form signed. Discuss risks and benefits. Ask for questions.
Your part in this study is confidential. None of the information will identify you by name. Scientific reports will be based on group data and will not identify you or any individual as being in this project. There are no anticipated risks from participating in this study. If you are not comfortable answering any of the questions asked, you may refuse to answer and/or refuse to participate any further. There will be no direct benefits to you for taking part in this study. Do you have any questions before we begin?
II. Main Interview I would like to talk with you today about water quality monitoring technologies used in coastal management. I am mostly interested in your thoughts on water quality technology and the reasons why you've chose to use them. We have divided the interview into three sections beginning with current water quality monitoring technologies, then we will talk about new monitoring technologies, and finally we will talk about future technological needs.

Background:
What is your title and responsibilities here at [name of organization/agency]? How long have you been at this job? At this position?

Section 1: Existing technologies & factors that affected adoption
In general, what are some aspects of water quality you study? (Probe for monitoring technology capability at addressing issues) What percentage of your time would you say you spend on water quality monitoring or water quality related issues?
In your opinion, what do you think about the current water quality conditions in Rhode Island's waters (both marine and fresh water)?
-How do you think these conditions compare to conditions in the past 15-20 years ago?
We would like to learn more about the tools you use to monitor water quality…. What tools do you use to monitor water quality?
I'm going to ask you some questions about each of the tools that you say you use. ( I would like to talk with you about a few of these new technologies: Are you familiar with any water quality monitoring technologies that use new monitoring techniques such as Lab on a Chip technologies such as environmental sample processors, visualization technologies such as Imaging Flow Cytobot, or autonomous nutrient sensors such as ISUS? -How much do you know (how familiar) about these technologies or others that I didn't mention? -What do you think of these kinds of technologies? (probe for attitudes, initial perceptions) -Do you use them?
o Have you considered using them? Why or why not?
The nanoscale biosensor that is currently being developed a URI will be a low cost, handheld device that will be used in the field to detect the concentration of algae or other biological components present in a water sample. The biosensor will utilize electromagnetic radiation in order to detect a range of wavelengths given off by algae species.
-What are your immediate thoughts of this technology? -Would you consider using this technology in your current position? Why or why not? -What types of applications do you think this device would be useful for? -Can you describe some of the characteristics of a device like this that would get you to use it?

Section 3: Technological gaps & future needs
How would you describe the changes of water quality instruments or research methods over the last 10-15 years? (probe for changes in technologies over the years)