Applications | Page 21 of 62 | Locus Technologies

Top Enhancements to Locus EHS Compliance Software in 2019

Let’s take a look back on the most exciting new features and changes made in Locus Platform during 2019!

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New Task Types

Two additional types of task periodicity have been added: Triggered tasks, which allow the automatic creation of a Task based on the creation of a triggering event (e.g., a spill or storm event), and Sequenced tasks, which allow the creation of a series of tasks in a designated order. Learn more about our compliance and task management here.[/sc_icon_with_text]

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Mobile Form Builder

Users can now create a mobile version of any data input form. Every form in the desktop platform can be mobile-enabled, so you can introduce new ways of streamlining data collection to your team.[/sc_icon_with_text]

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Process Flow

‘Process Flows’ have been added, which guide users in completing processes following a simple step-by-step interface.[/sc_icon_with_text]

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Expanded Facilities Management App

Our expanded Facilities Management App is designed to map at the enterprise level showing all locations, navigate your facilities hierarchy to review information and quickly take action at every level. Locus Facilities is a comprehensive facility management application that aims to increase the efficiency of customer operations and centralize important company information.[/sc_icon_with_text]

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User Configurable Dashboards

Users can choose from existing portlets (found on the dashboard pages) to customize their landing page to their unique needs. Create custom dashboards to highlight exactly the information you want in any format (charts, maps, tables, tree maps, diagrams, and more).[/sc_icon_with_text]

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Edit via Email

Add notes to any record by sending an email directly into the system. Allows anyone to add or append to a record in the system simply through email.[/sc_icon_with_text]

Contact us to see a demo of Locus Platform

California’s Low Carbon Fuel Standard Program

Last week Locus attended the first training session offered by California Air Resources Board (CARB) for verifiers under the California Low Carbon Fuel Standard (LCFS) program. The California LCFS program has been ramping up over the past several years, and is now ready to start certifying third-party verifiers to review both applications and routine reporting.

The LCFS program is part of California’s initiative to meet the AB32 requirements of reducing overall greenhouse gas emissions to 1990 levels by 2020, and 40% lower than that level by 2030. LCFS is specifically intended to address emissions from transportation fuels in California, which are approximately half of the overall emissions statewide. Like the Greenhouse Gas Mandatory Reporting Rule and Cap-and-Trade programs that preceded it, the California LCFS program uses a market-based approach to incentivize innovation and new approaches to reduce emissions.

LCFS Expert Seth Lalonde at the California Air Resources Board Training

Seth Lalonde, Locus Environmental Scientist, at the California Air Resources Board Training

The program covers a wide variety of projects, including production of alternative fuels (e.g. renewable diesel and biogenic compressed natural gas), innovative approaches to fossil fuel production and refining, and direct carbon capture and sequestration. Fuels are assigned a carbon intensity based on overall carbon dioxide emissions over the entire life cycle, from production to processing to shipping to consumption. The carbon intensity is essentially a measure of the emissions from the fuel per unit of energy. The lower the carbon intensity value, the less impact the fuel has in terms of carbon emissions. Certain fuels can even have a negative carbon intensity, which essentially means the fuel production process is absorbing more carbon than is eventually emitted to the atmosphere (such is the case for compressed or liquefied natural gas produced using biomethane from manure collection). The program also has impacts well outside the California border. After all, fuel that is eventually used in California can originate anywhere in the world, and the LCFS program allows for these projects to obtain credits regardless of their location.

Unsurprisingly, California was the first state to adopt and implement a LCFS program, and the first to establish a third-party verification program specific for LCFS. Although it was clearly the first presentation of this training material, staff from CARB as well as the Climate Action Reserve and The Climate Registry were on hand to assist in addressing questions and topics that weren’t covered in the prepared materials. And considering the wide variety of LCFS project types and the disparate backgrounds of attendees for the verification training, they did a great job of getting everyone all the information they needed to understand and verify these projects.

For those participating in the LCFS program or considering projects under the program, there are a few key things to keep in mind.

First and foremost, like any market-based emission program that includes a verification or auditing requirement, transparency is critical. The verifiers are trained to dig deep into your data, and not to take ‘no’ for an answer. Be prepared to have your metadata and documentation assembled and easily made available to the verifier. (For more on Transparency in Reporting, view this webinar)

Second, the LCFS program includes requirements for continuous or near-continuous monitoring for many parameters, and instrumentation capable of electronic data archival. Manual data records and transcription are still acceptable under other carbon offset programs, but under LCFS these options are no longer allowed. Be sure that your instrumentation is consistent with the specific LCFS requirements, or you’ll be seeing a non-conformance from your verifier.

There were many other tips and common pitfalls highlighted during the training for specific LCFS project types. Overall, I’m very excited to see how the LCFS program evolves in California, and how the energy industry takes advantage of these incentives to provide new options for transportation fuels that will reduce carbon emissions.

Update: Locus is now an approved verification body for the Low Carbon Fuel Standard. Learn more here.

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Mapping All of Space and Time

Today is GIS Day, a day started in 1999 to showcase the many uses of geographical information systems (GIS). To celebrate the passage of another year, this blog post examines how maps and GIS show time, and how Locus GIS+ supports temporal analysis for use with EIM, Locus’s cloud-based, software-as-a-service application for environmental data management.

Space and Time

Since GIS was first imagined in 1962 by Roger Tomlinson at the Canada Land Inventory, GIS has been used to display and analyze spatial relationships. Every discrete object (such as a car), feature (such as an acre of land), or phenomenon (such as a temperature reading) has a three-dimensional location that can be mapped in a GIS as a point, line, or polygon. The location consists of a latitude, longitude, and elevation. Continuous phenomenon or processes can also be located on a map. For example, the flow of trade between two nations can be shown by an arrow connecting the two countries with the arrow width indicating the value of the traded goods.

However, everything also has a fourth dimension, time, as locations and attributes can change over time. Consider the examples listed above. A car’s location changes as it is driven, and its condition and value change as the car gets older. An acre of land might start covered in forest, but the land use changes over time if the land is cleared for farming, and then later if the land is paved over for a shopping area. The observed temperature at a given position changes with time due to weather and climate changes spanning multiple time scales from daily to epochal. Finally, the flow of trade between two countries changes as exports, imports, and prices alter over time.

Maps and Time

Traditional flat maps already collapse three dimensions into two, so it’s not surprising that such maps do not handle the extra time dimension very well. Cartographers have always been interested in showing temporal data on maps, though, and different methods can be employed to do so. Charles Minard’s famous 1861 visualization of Napoleon’s Russian campaign in 1812-1813 is an early example of “spatial temporal” visualization. It combines two visuals – a map of troop movements with a time series graph of temperature – to show the brutal losses suffered by the French army. The map shows the army movement into Russia and back, with the line width indicating the troop count. Each point on the chart is tied to a specific point on the map. The viewer can see how troop losses increased as the temperature went from zero degrees Celsius to -30 degrees. The original thick tan line has decreased to a black sliver at the end of the campaign.

Charles Minard’s map of Napoleon’s Russian campaign in 1812-1813.

The Minard visual handles time well because the temperature chart matches single points on the map; each temperature value was taken at a specific location. Showing time changes in line or area features, such as roads or counties, is harder and is usually handled through symbology. In 1944, the US Army Corps of Engineers created a map showing historical meanders in the Mississippi River. The meanders are not discrete points but cover wide areas. Thus, past river channels are shown in different colors and hatch patterns. While the overlapping meanders are visually complex, the user can easily see the different river channels. Furthermore, the meanders are ‘stacked’ chronologically, so the older meanders seem to recede into the map’s background, similar to how they occur further back in time.

Inset from Geological Investigation of the Alluvial Valley of the Lower Mississippi River.

Another way to handle time is to simply make several maps of the same features, but showing data from different times. In other words, a temporal data set is “sliced” into data sets for a specific time period. The viewer can scan the multiple maps and make visual comparisons. For example, the Southern Research Station of the US Forest Service published a “report card” in 2011 for Forest Sustainability in western North Carolina. To show different land users over time, small maps were generated by county for three years. Undeveloped land is colored green and developed land is tan. Putting these small maps side by side shows the viewer a powerful story of increasing development as the tan expands dramatically. The only drawback is that the viewer must mentally manipulate the maps to track a specific location.

Land Use change over time for Buncombe County, NC

GIS and Time

The previous map examples prove that techniques exist to successfully show time on maps. However, such techniques are not widespread. Furthermore, in the era of “big data” and the “Internet of Things”, showing time is even more important. Consider two examples. First, imagine a shipment of 100 hazardous waste containers being delivered on a truck from a manufacturing facility to a disposal site. The truck has a GPS unit which transmits its location during the drive. Once at the disposal site, each container’s active RFID tag with a GPS receiver tracks the container’s location as it proceeds through any decontamination, disposal, and decommission activities. The locations of the truck and all containers have both a spatial and a temporal component. How can you map the location of all containers over time?

As a second example, consider mobile data collection instruments deployed near a facility to check for possible contamination in the air. Each instrument has a GPS so it can record its location when the instrument is periodically relocated. Each instrument also has various sensors that check every minute for chemical levels in the air plus wind speed and temperature. All these data points are sent back to a central data repository. How would you map chemical levels over time when both the chemical levels and the instrument locations are changing?

In both cases, traditional flat maps would not be very useful given the large amounts of data that are involved. With the advent of GIS, though, all the power of modern computers can be leveraged. GIS has a powerful tool for showing time: animation. Animation is similar to the small “time slice” maps mentioned above, but more powerful because the slices can be shown consecutively like a movie, and many more time slices can be created. Furthermore, the viewer no longer has to mentally stack maps, and it is easier to see changes over time at specific locations.

Locus has adopted animation in its GIS+ solution, which lets a user use a “time slider” to animate chemical concentrations over time. When a user displays EIM data on the GIS+ map, the user can decide to create “time slices” based on a selected date field. The slices can be by century, decade, year, month, week or day, and show the maximum concentration over that time period. Once the slices are created, the user can step through them manually or run them in movie mode.

To use the time slider, the user must first construct a query using the Locus EIM application. The user can then export the query results to the GIS+ using the time slider option. As an example, consider an EIM query for all benzene concentrations sampled in a facility’s monitoring wells since 2004. Once the results are sent to the GIS+, the time slider control might look like what is shown here. The time slices are by year with the displayed slice for 3/30/2004 to 3/30/2005. The user can hit play to display the time slices one year at a time, or can manually move the slider markers to display any desired time period.

Locus GIS+ time slider

Here is an example of a time slice displayed in the GIS+. The benzene results are mapped at each location with a circle symbol. The benzene concentrations are grouped into six numerical ranges that map to different circle sizes and colors; for example, the highest range is from 6,400 to 8,620 µg/L. The size and color of each circle reflect the concentration value, with higher values corresponding to larger circles and yellow, orange or red colors. Lower values are shown with smaller circles and green, blue, or purple colors. Black squares indicate locations where benzene results were below the chemical detection limit for the laboratory. Each mapped concentration is assigned to the appropriate numerical range, which in turn determines the circle size and color. This first time slice for 2004-2005 shows one very large red “hot spot” indicating the highest concentration class, two yellow spots, and several blue spots, plus a few non-detects.

Time slice for a year for a Locus GIS+ query

Starting the time slider runs through the yearly time slices. As time passes in this example, hot spots come and go, with a general downward trend towards no benzene detections. In the last year, 2018-2019, there is a slight increase in concentrations. Watching the changing concentrations over time presents a clear picture of how benzene is manifesting in the groundwater wells at the site.

GIS+ time slider in action

While displaying time in maps has always been a challenge, the use of automation in GIS lets users get a better understanding of temporal trends in their spatial data. Locus continues to bring new analysis tools to their GIS+ system to support time data in their environmental applications.

Time slice for a Locus GIS+ query

Interested in Locus’ GIS solutions?

Locus GIS+ features all of the functionality you love in EIM’s classic Google Maps GIS for environmental management—integrated with the powerful cartography, interoperability, & smart-mapping features of Esri’s ArcGIS platform!

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[sc_image width=”150″ height=”150″ src=”16303″ style=”11″ position=”centered” disable_lightbox=”1″ alt=”Dr. Todd Pierce”]

About the Author—Dr. Todd Pierce, Locus Technologies

Dr. Pierce manages a team of programmers tasked with development and implementation of Locus’ EIM application, which lets users manage their environmental data in the cloud using Software-as-a-Service technology. Dr. Pierce is also directly responsible for research and development of Locus’ GIS (geographic information systems) and visualization tools for mapping analytical and subsurface data. Dr. Pierce earned his GIS Professional (GISP) certification in 2010.

Top 8 Things to Look For in Sustainability Software

Sustainability is a corporate necessity, and finding the right software to support company-wide sustainability goals and initiatives is imperative to streamlining this time-consuming activity. This is especially true if you are managing inputs from many facilities/locations or have required or optional reporting requirements. Not to mention, most corporate annual reports demand a summary of key sustainability initiatives as part of the corporate annual reporting process.

Here are some features to look for when selecting a sustainability software—to make sure your new software will actually help your company track and report its sustainability initiatives more accurately and efficiently.

1. Make sure software is accessible to everyone who needs to input data

It is very important that data owners/data collectors throughout your facilities can directly enter their own relevant Key Performance Indicator (KPI) and greenhouse gas data—no more searching for data from disparate company groups, or searching through email for spreadsheets or invoices, and no more tracking down the field technician for the field log, or hunting for other assorted documentation.

This is especially important when dealing with company locations in various geographic regions. A well-designed software system can solve this most vexing problem: finding the relevant data.

Check for the following features in any sustainability software you’re considering:

Data stored in one managed location
All sustainability data should be stored in one place—whether text or numeric, and whether from an automatic data acquisition system, external database, hand-written field logs, or third-party documentation (e.g., air permits).
Streamlined reporting from centralized data
Reporting is streamlined because all input is consolidated in one managed location.
Standardized terminology and units
A centralized system enforces common terminology, units, and values (numbers vs. text) that are so important for final reporting. No one wants to get energy data from 10 different sources, all in different units, formats, and terminologies.
Built-in notifications and workflows
Also, look for built-in reminders, notifications, and escalations to ensure the inputs are completed in a timely manner, and if deadlines are missed, you know exactly what is missing and who to contact.

Data can come from multiple sources, and your sustainability software should be able to handle them all—then consolidate this data into a single source of truth.

2. Make sure the software application includes quality assurance and third-party review tools

Any decent software can make data collection easy, but to truly improve your company’s sustainability initiatives, it must also have tools for quality assurance reviewers and third-party verifiers to easily review the information, track the reported values to source data, and understand how the data were processed. Ultimately, the software also needs to allow the reporter to easily make updates or corrections as needed. Because these data are reported to regulators or shareholders, accuracy is paramount.

Look for the following features to support transparency and auditing:

Visible and accessible calculations
All embedded rules, queries, and calculations should be visible and traceable to anyone reviewing so they can check the calculations and raise a flag if issues are found.

Your sustainability software should make it easy to see and understand the formulas that produced any calculated data values.
Accessible and auditable source data and final values
All source data and final reported values should be visible, traceable, and tracked. Watch out for “black box” calculations that will confound auditors and cost you in labor hours while you are determining how the reported value was obtained, what the data inputs were, and where the source data originated.
Complete audit trails
Ensure audit trails are present for any changes in key data. You should be able to find out exactly who entered a value or who changed it. Be sure the software is keeping track and that everything is recorded and traceable to ensure the integrity of the process and reports. Good software will have an audit tool that tracks who did what, who is responsible for which datasets, and who changed which values and how.

3. Make sure the software includes tools for reporting to multiple regulatory or voluntary bodies

Many companies report to various regulatory or voluntary bodies, and the software you select should support all the major reporting requirements to avoid the need for separate calculations for some jurisdictions.

Enter once, report 10x
Look for the concept of “enter once, report many times” when reviewing software applications. The gold standard is the capability for reporting methodologies and calculations configured for reporting to multiple agencies from a single dataset, all in a single tool.
Check support for your actual, specific needs
Review your reporting requirements to see if the software handles them. Key reporting requirements include state or federal regulations, internal corporate social responsibility (CSR) and other sustainability reporting, the Carbon Disclosure Project (CDP), Global Reporting Initiative (GRI), and The Climate Registry (TCR).
Consider export formats
Ensure the software includes exports to XML, which is a common format for EPA and ARB reporting, and an option for reporting to other agencies. Having such outputs easily generated from the software will save time and money during the reporting season.

Find out what formats you need for regulatory reporting, and make sure your software supports exporting in these formats.

4. Look for data verification flags so you don’t spend time fixing obviously bad data

If you normally report 500 metric tons of GHG per year and you are finding entries of 500,000 metric tons per year in your data, chances are, it’s just simple data entry errors. However, no one wants to track these down months after the data entry event. Look for software that will flag these anomalies on entry and force the user to fix them before you ever get to the data review step.

Ability to set validation rules
Look for software that allows you to set rules to flag data entries that fall outside of expected thresholds, catching errors before they make it to QA personnel or auditors.
Options to specify acceptable ranges and add comments for unusual values
Look for features that will help you avoid last-minute questions about the validity of your data. Look for the ability to specify an outlier range to flag values so that you can address them immediately before the report is due. Allow for the opportunity to enter a comment right alongside the flagged value, providing a record that the value was double-checked and is correct for a specified reason.

Immediate, inline alerts about outlier data values help prevent last-minute surprises.

5. Look for user-defined workflows to help you and your users step through sustainability reporting and tracking process

The sustainability software you select should help simplify data entry and reporting by supporting your preferred workflows. Software with configurable workflows can be a huge help for both data entry personnel and managers reviewing data, by making the status of all data entry and reporting business processes abundantly clear.

Options for lockdown after manager review
Look for the ability to include manager overrides to data entry and workflows that will lock the data entries to editing once reviewed. This will help ensure others are not modifying data while you are in the report preparation process.

Options for managers to lock down data are important for preventing edits to data that is being prepared for reporting.
Quickly identify current workflow status
Check for easy visual indicators of workflow status to ensure the process is on track to be completed by the reporting deadline.

There should be an easy way to see the current workflow status of any data in your system.
Easily modify workflow along the way
Also look for the ability to easily modify the workflow if your original configuration was not optimal. Not everyone knows the best workflow for new software when they initially start using it. The ability to modify the workflows—without needing a software developer—is an important feature to consider when choosing a sustainability software solution.

6. Look for robust audit trails to help solve “whodunit” issues

All software that handles critical or regulatory data should provide auditing on key data fields. Find out the details of what is audited and how you will be able to access the audit information.

Full history of all changes
Software should retain a history of values with every report change.
Who, when, what
Look for a complete audit trail of who did what, and what was changed, and when. Tracking any modifications to values supports a rigorous audit and is sure to make your QC staff really happy.

Your software should be automatically recording a history of all changes at each step of your workflow.

7. Look at out-of-the-box data outputs—but also consider how easy (or hard) it will be to create specific reports for your corporate needs

Every software has built-in report and dashboards, but they may not meet all your needs out-of-the-box. Assume some reports will need to be configured, and review the software accordingly.

Tracking specific KPIs
Does the software provide an easy way to track year-to-year KPIs for internal evaluation or for preparation of public-facing sustainability reports?
Consider future reporting and visualization needs
If you need a new report, chart, or other visualization of your data, will this request incur a custom software development charge, or is it an easy configuration?
Adapt dashboards to your needs
Can you easily customize the software’s default dashboards?

Look for options to easily configure reports, charts, and other visualizations that help you easily review summaries of your data.

8. Make sure the software has a robust notification engine

Software can shoulder the burden of getting people to do what they are supposed to do (reminders), alerting people to when an action is needed (notifications), sharing information (messaging) and sending them information (report notifications). Be sure to review the strength of all notification features of the software, as this can be a huge help during reporting season—and it can lighten the burden on your inbox as well.

Multi-purpose notifications
Look for routine workflow notifications to ensure you are notified when a workflow step is completed AND if a workflow step is ignored beyond the due date.
Actionable notifications
Look for reporting notifications that will send the link (URL) to applicable users so they can quickly jump to the information in the software. No one likes knowing a report is ready, but then having to log in and search for it.
Group and individual notifications
Ensure you can send notifications by individual user OR to user groups. It can be very tedious to select large numbers of individuals for routine notifications—it is much easier to select “all Facility XYZ EHS staff”.
Decide where to receive notifications
Consider in-app messaging to keep important information in front of the users and spare their inbox.

Final thoughts: Imagine what implementation success looks like

While you are evaluating software options, use these points as a guide to make sure you choose a solution that will truly make a difference for your organization’s sustainability initiatives and reporting goals.

As more sustainability software solutions appear in the marketplace, it can be difficult for a company to discern which features really matter for its workflow. Try a simple exercise—imagine what a perfect sustainability management business process would look like if you found the perfect software solution. Consider the challenges you face now, and what it would look like if those problems were handled by your software.

Then, ask how well the sustainability software you’re considering will make this dream a reality. The right software selection can help reduce operational risk, fulfill regulatory reporting requirements in less time and with less effort, and provide safeguards against bad data and missed deadlines. All you have to do is ask the right questions.

Get more tips for what to look for when evaluating EHS&S software!

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When it comes to EHS&S, the “&S” shouldn’t be an afterthought

Locus Technologies is proud to have attended this year’s NAEM EHS&S Forum in Toronto. We were represented by Wes Hawthorne, President of Locus, and forum first-timer, Aaron Edwards, Marketing Associate at Locus.

The forum gave us the opportunity to learn, both from our peers in discussions about EHS&S goals, and from the diverse lineup of respected speakers and presenters. You spoke and we listened. This year, the prevailing topic of discussion was the growth of expectation surrounding sustainability in organizations.

Sustainability initiatives are rapidly moving to the forefront of institutional policy at leading organizations. As consumers, investors, and shareholders are increasingly supporting more sustainable organizations, executives are expecting more impactful sustainability initiatives from their EHS&S departments. Not only that, but executives inherently expect sustainability initiatives to positively affect the bottom line. This means that today’s EHS&S professionals have to manage sustainability initiatives that are vital to company success as well as regulatory management and reporting, often with limited resources.

Our conversations at the NAEM Forum often revolved around the time-consuming nature of regulatory compliance interfering with the escalated focus on sustainability. Many of the professionals we spoke with are dealing with multiple EHS&S platforms, each used for a specific function. Time management is increasingly more essential to EHS&S managers, and juggling between uni-tasked platforms is detrimental to effective sustainability efforts.

Locus developers have designed our software to reduce the labor-intensiveness of regulatory compliance. We offer a configurable single-platform solution for decreasing the amount of time you spend managing KPIs. From available modules in waste management, audit tracking, GHG reporting, and more⁠—our configurable software allows more time to improve your company’s sustainability initiatives.

Sustainability is no longer an afterthought in the eyes of executives, consumers, investors, or shareholders. Having one robust software platform can greatly help EHS professionals integrate that “&S” seamlessly with their other responsibilities.

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EPA to set tougher requirements for lead in water

The Environmental Protection Agency (EPA) announced that it would impose stricter requirements on water utilities to manage lead and copper contamination in drinking water supplies. The EPA said that tackling water pollution is a core duty of the agency.

The proposed changes, the first affecting lead level in water since 1991, would also give utilities more time to replace lead pipes in their systems. Some environmental groups are not happy with the proposed rule because the change slows by 20 years the timeline for removing aging lead service pipes that could expose children to lead. Lead is a toxin known to harm developing brains. The rule slows down the removal of pipelines where lead levels exceed 15 μg/L to 33 years from the 13 years in the original law.

The new rule requires water utilities to identify and remove sources of lead when a water sample at faucet exceeds 15 micrograms per liter (μg/L). The EPA said water systems would also have to follow new, improved sampling procedures and adjust sampling sites to better target locations with higher lead levels.

Health advocates estimate that as many as six million or more lead water lines remain underground in U.S. cities and towns. Additional sampling and monitoring can help to identify affected areas, and ensure the quality of drinking water sources.

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Predicting Water Quality with Machine Learning

At Locus Technologies, we’re always looking for innovative ways to help water users better utilize their data. One way we can do that is with powerful technologies such as machine learning. Machine learning is a powerful tool which can be very useful when analyzing environmental data, including water quality, and can form a backbone for competent AI systems which help manage and monitor water. When done correctly, it can even predict the quality of a water system going forward in time. Such a versatile method is a huge asset when analyzing data on the quality of water.

To explore machine learning in water a little bit, we are going to use some groundwater data collected from Locus EIM, which can be loaded into Locus Platform with our API. Using this data, which includes various measurements on water quality, such as turbidity, we will build a model to estimate the pH of the water source from various other parameters, to an error of about 1 pH point. For the purpose of this post, we will be building the model in Python, utilizing a Jupyter Notebook environment.

When building a machine learning model, the first thing you need to do is get to know your data a bit. In this case, our EIM water data has 16,114 separate measurements. Plus, each of these measurements has a lot of info, including the Site ID, Location ID, the Field Parameter measured, the Measurement Date and Time, the Field Measurement itself, the Measurement Units, Field Sample ID and Comments, and the Latitude and Longitude. So, we need to do some janitorial work on our data. We can get rid of some columns we don’t need and separate the field measurements based on which specific parameter they measure and the time they were taken. Now, we have a datasheet with the columns Location ID, Year, Measurement Date, Measurement Time, Casing Volume, Dissolved Oxygen, Flow, Oxidation-Reduction Potential, pH, Specific Conductance, Temperature, and Turbidity, where the last eight are the parameters which had been measured. A small section of it is below.

Alright, now our data is better organized, and we can move over to Jupyter Notebook. But we still need to do a bit more maintenance. By looking at the specifics of our data set, we can see one major problem immediately. As shown in the picture below, the Casing Volume parameter has only 6 values. Since so much is missing, this parameter is useless for prediction, and we’ll remove it from the set.

We can check the set and see that some of our measurements have missing data. In fact, 261 of them have no data for pH. To train a model, we need data which has a result for our target, so these rows must be thrown out. Then, our dataset will have a value for pH in every row, but might still have missing values in the other columns. We can deal with these missing values in a number of ways, and it might be worth it to drop columns which are missing too much, like we did with Casing Volume. Luckily, none of our other parameters are, so for this example I filled in empty spaces in the other columns with the average of the other measurements. However, if you do this, it is necessary that you eliminate any major outliers which might skew this average.

Once your data is usable, then it is time to start building a model! You can start off by creating some helpful graphs, such as a correlation matrix, which can show the relationships between parameters.

For this example, we will build our model with the library Keras. Once the features and targets have been chosen, we can construct a model with code such as this:

This code will create a sequential deep learning model with 4 layers. The first three all have 64 nodes, and of them, the initial two use a rectified linear unit activation function, while the third uses a sigmoid activation function. The fourth layer has a single node and serves as the output.

Our model must be trained on the data, which is usually split into training and test sets. In this case, we will put 80% of the data into the training set and 20% into the test set. From the training set, 20% will be used as a validation subset. Then, our model examines the datapoints and the corresponding pH values and develops a solution with a fit. With Keras, you can save a history of the reduction in error throughout the fit for plotting, which can be useful when analyzing results. We can see that for our model, the training error gradually decreases as it learns a relationship between the parameters.

The end result is a trained model which has been tested on the test set and resulted in a certain error. When we ran the code, the test set error value was 1.11. As we are predicting pH, a full point of error could be fairly large, but the precision required of any model will depend on the situation. This error could be improved through modifying the model itself, for example by adjusting the learning rate or restructuring layers.

You can also graph the true target values with the model’s predictions, which can help when analyzing where the model can be improved. In our case, pH values in the middle of the range seem fairly accurate, but towards the higher values they become more unreliable.

So what do we do now that we have this model? In a sense, what is the point of machine learning? Well, one of the major strengths of this technology is the predictive capabilities it has. Say that we later acquire some data on a water source without information on the pH value. As long as the rest of the data is intact, we can predict what that value should be. Machine learning can also be incorporated into examination of things such as time series, to forecast a trend of predictions. Overall, machine learning is a very important part of data analytics and the development of powerful AI systems, and its importance will only increase in the future.

What’s next?

As the technology around machine learning and artificial intelligence evolves, Locus will be working to integrate these tools into our EHS software. More accurate predictions will lead to more insightful data, empowering our customers to make better business decisions.

Contact us today to learn how machine learning and AI can help your EHS program thrive

Infographic: 6 Benefits of EHS on AWS

In this infographic, we have outlined a few of the ways EHS programs benefit from having an AWS-hosted solution. Locus customers recently received these benefits as a result of moving our entire infrastructure to Amazon Web Services—the world’s leading cloud. Learn more about the move to AWS.

Contact us to learn more about these benefits

Artificial Intelligence and Environmental Compliance–Revisited–Part 4: AI, Big Data + Multi-Tenancy = The Perfect System

AI and Big Data to Drive EHS Decisions via Multi-tenant SaaS

With data and information streaming from devices like fire hydrants, there is little benefit from raw data, unless a company owning the data has a way to integrate the data into its record system and pair it with regulatory databases and GIS. That is where the advancement in SaaS tools and data sources mashups has helped set the stage for AI as a growing need.

Humans are not very good at analyzing large datasets. This is particularly true with data at the planetary level that are now growing exponentially to understand causes and fight climate change. Faced with a proliferation of new regulations and pressure to make their companies “sustainable” EHS departments keep adding more and more compliance officers, managers, and outside consultants, instead of investing in technology that can help them. Soon, they will be turning to AI technology to stay on top of the ever-changing regulatory landscape.

AI, in addition to being faster and more accurate, should make compliance easier. Companies spend too much time and effort on the comprehensive quarterly or annual reporting—only to have to duplicate the work for the next reporting period. The integrated approach, aided by AI, will automate these repetitive tasks and make it easier than just having separate analyses performed on every silo of information before having a conversation with regulators.

In summary, whether it is being used to help with GHG emissions monitoring and reporting, water quality management, waste management, incident management, or other general compliance functions, AI can improve efficiency, weed out false-positive results, cut costs and make better use of managers’ time and company resources.

Another advantage of AI, assuming it is deployed properly, concerns its inherent neutrality on data evaluation and decision making. Time and time again we read in the papers about psychological studies and surveys that show people on opposite sides of a question or topic cannot even agree on the “facts.” It should not be surprising then to find that EHS managers and engineers are often limited by their biases. As noted in the recent best-seller book by Nobel Memorial Prize in Economics laureate Daniel Kahneman, “Thinking, Fast and Slow,” when making decisions, they frequently see what they want, ignore probabilities, and minimize risks that uproot their hopes. Even worse, they are often confident even when they are wrong. Algorithms with AI built-in are more likely to detect our errors than we are. AI-driven intelligent databases are now becoming powerful enough to help us reduce human biases from our decision-making. For that reason, large datasets, applied analytics, and advanced charting and data visualization tools, will soon be driving daily EHS decisions.

In the past, companies almost exclusively relied upon on-premise software (or single-tenant cloud software, which is not much different from on-premise). Barriers were strewn everywhere. Legacy systems did not talk to one another, as few of the systems interfaced with one another. Getting data into third-party apps usually required the information to be first exported in a prescribed format, then imported to a third-party app for further processing and analysis. Sometimes data was duplicated across multiple systems and apps to avoid the headache of moving data from one to another. As the world moves to the multi-tenant SaaS cloud, all this is now changing. Customers are now being given the opportunity to analyze not just their company’s data, but data from other companies and different but potentially related and coupled categories via mashups. As customers are doing so, interesting patterns are beginning to emerge.

The explosion of content—especially unstructured content—is an opportunity and an obstacle for every business today.

The emergence of artificial intelligence is a game-changer for enterprise EHS and content management because it can deliver business insights at scale and make EHS compliance more productive. There are numerous advantages when you combine the leading multi-tenant EHS software with AI:

Ability to handle the explosion of unstructured content where legacy on-premise EHS solutions can’t.
AI can organize, illuminate, and extract valuable business insights if all your content is managed in one secure location in the cloud.
Locus helps you take advantage of best-of-breed AI technologies from industry leaders and apply them to all your content.

We are seeing in the most recent NAEM white paper, Why Companies Replace Their EHS&S Software Systems, that people want the ability to integrate with other systems as a top priority. Once the ability to share/consolidate data is available, AI is not far behind in the next generation of EHS/Water Quality software.

This concludes the four-part blog series on Big Data, IoT, AI, and multi-tenancy. We look forward to feedback on our ideas and are interested in hearing where others see the future of AI in EHS software – contact us for more discussion or ideas! Read the full Series: Part One, Part Two, Part Three.

Contact us to learn more about Locus uses IoT and AI

Locus Technologies goes all-in on AWS

SAN FRANCISCO, Calif., 23 July 2019 — Locus Technologies (Locus), the market leader in multi-tenant SaaS water quality, environmental compliance, and sustainability management, today announced that it is going all-in on Amazon Web Services, Inc. (AWS), moving its entire infrastructure to the world’s leading cloud. By moving its flagship product EIM (Environmental Information Management) to AWS this month, Locus will complete its transition to AWS. Locus previously moved its Locus Platform (LP) to AWS in 2018.

EIM is the world’s leading water quality management software used by many Fortune 500 companies, water utilities, and the US Government since 1999. Among its many features, EIM delivers real-time tools to ensure that water utilities deliver clean water to consumers’ taps and don’t discharge contaminated wastewater above allowable limits to groundwater or surface water bodies like streams, lakes, or oceans.

EIM generates big data, and with over 500 million analytical records at over 1.3 million locations worldwide, it is one of the largest centralized, multi-tenant water quality management SaaS systems in the world. With anticipated growth in double digits stemming from the addition of streaming data from sensors and many IoT monitoring devices, Locus needed to have a highly scalable architecture for its software hosting. The unmatched performance and scalability of AWS’s offerings are just the right match for powering Locus’ SaaS.

Because of the scope of its applications, Locus is expecting to leverage the breadth and depth of AWS’s services (including its database systems, serverless architecture, IoT streaming, blockchain, machine learning, and analytics) to automate and enhance the on-demand EHS compliance, sustainability, facility, water, energy, and GHG management tools that Locus’ software provides to its customers.

Running on AWS’s fault-tolerant and highly performant infrastructure will help support Locus’s everyday business, and will scale easily for peak periods, where reporting demand such as GHG calculation engine or significant emissions incidents like spills can skyrocket scalability demand.

By leveraging Amazon CloudFront, Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Kubernetes Service (Amazon EKS) and AWS Lambda, Locus is migrating to a microservices architecture to create more than 150 microservices that independently scale workloads while reducing complexity in the cloud, thereby enhancing every element of the customer cloud experience. Locus built a data lake on Amazon Simple Storage Service (Amazon S3) and will leverage Amazon Redshift to analyze the vast amount of data it is storing in the cloud, delivering insights and predictive analytics that uncover chemicals trending patterns and predict future emissions releases at various locations.

Locus intends to leverage AWS IoT services and Amazon Managed Blockchain by building a new native integration to help businesses generate value from the millions of events generated by connected devices such as real-time environmental monitoring sensors and environmental treatment systems controls. AWS IoT is a set of cloud services that let connected devices easily and securely interact with cloud applications like EIM and Locus Platform and other devices. Locus IoT Cloud on AWS allows customers to experience real-time emissions monitoring and management across all their connected sensors and devices. And for customers who want to allow multiple parties to transact (e.g. GHG trading) without a trusted central authority, AWS provides a fully managed, scalable blockchain service. Amazon Managed Blockchain is a fully managed service makes it easy to setup, deploy, and manage scalable blockchain networks that Locus intends to use for emissions management and trading.

For example, a water utility company that maintains thousands of IoT-enabled sensors for water flow, pressure, pH, or other water quality measuring devices across their dispersed facilities and pipeline networks will be able to use Locus IoT on AWS to ingest and manage the data generated by those sensors and devices, and interpret it in real time. By combining water sensor data with regulatory databases, water utility companies will be able to automatically create an emergency shutdown if chemical or other exceedances or device faults are detected and as such, will be better prepared to serve their customers and environment.

By combining the powerful, actionable intelligence in EIM and rapid responsiveness through Locus Platform with the scalability and fast-query performance of AWS, customers will be able to analyze large datasets seamlessly on arrival in real time. This will allow Locus’ customers to explore information quickly, find insights, and take actions from a greater variety and volume of data—all without investing the significant time and resources required to administer a self-managed on-premises data warehouse.

“After 22 years in business, and after evaluating AWS for a year with our Locus Platform, we decided to switch and continue all our business on AWS. We are taking advantage of their extensive computing power, depth and breadth of services and expertise to develop an effective cloud infrastructure to support our growing business and goal of saving the planet Earth by providing and managing factual information on emissions management, all the while reducing operational costs of Locus’ customers,” said Neno Duplan CEO of Locus. “By operating on AWS, we can scale and innovate quickly to provide new features and improvements to our services – such as blockchain-based emissions management – and deliver exceptional scalability for our enterprise customers. With AWS, we don’t have to focus on the undifferentiated heavy lifting of managing our infrastructure, and can concentrate instead on developing and improving apps and services.”

“By organizing and analyzing environmental, sustainability, and water quality information in the cloud, Locus is helping organizations to understand the impact of climate change on drinking water,” said Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. “AWS’s unmatched portfolio of cloud services, proven operational expertise, and unmatched reliability will help Locus to further automate environmental compliance for companies ranging from local water utilities to multinational manufacturing corporations, to federal government research agencies. ”By choosing to go all-in on AWS, Locus is able to innovate and expand globally, developing new solutions that will leverage comprehensive analytics and machine learning services to gain deeper insights and forecast sustainability metrics that will help deliver clean drinking water to consumers around the world.”

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