Advantages of Proper Environmental Data Management

Many types of current data were still being collected; groundwater (levels and quality), waste production, dust, and other air quality variables from the emission stacks (VOCs, NOx, SOx, particulates), surface water, effluent quality, and flow rates, and weather. Data was also being collected on water use and recycling, fuel use (all sources) and the waste being recycled and landfilled.

Some of this data was manually collected, some resulted from the analysis by external labs and some were automatically collected from data loggers. Because the growth in data management had happened organically, different, and disparate, processes had evolved in parallel to deal with this. The processes were in part driven by the data source (e.g. the format in which data was provided by a lab or data logger) and in part by of the idiosyncrasies of the people involved.

The net result was that lots of people were doing lots of things but working inefficiently. Regular reporting of data was error-prone and reports were often late. The reporting process was a matter of cutting and pasting, sometimes from five different spreadsheets, to make a single report.

Our conclusion was that the V.P. was right – his data was in a mess and, without intervention, was likely to worsen. One such operational practice is usually an on-going monitoring program, with defined targets for many of the measured variables. The frequency of the monitoring can be anything from continuous (e.g. for dust, flow rates, vibration, or weather), hourly, daily, weekly, monthly, quarterly, half-yearly or annually. Breaches need to be addressed and reported to the control authority, sometimes within 24 hours of the incident.

Remedial measures need to be put in place, and repeated exceedances can, in extreme cases, result in prosecution, fines, or even withdrawal of permits or shutting down a facility. The data also needs to be reported internally and externally in differing formats and at regular intervals (monthly, quarterly, biannually or annually).

In addition to statutory permits, other legislation is in place in many jurisdictions that obligate qualifying companies to collect and provide information on the release of pollution. Such legislation is driven by public pressure. Examples include:

• The United States’ Toxics Release Inventory, born from the Emergency Planning and Community Right to Know (EPCRA) legislation,
• Australia’s National Pollution Inventory (NPI),
• Canada’s National Pollution Release Inventory and
• The European Pollutant Release and Transfer Register (E-PRTR), which contains data reported annually from some 24,000 industrial facilities covering 65 economic activities.

With rising concerns over climate change, many countries have also committed some or all of their industries to mandatory reporting requirements on greenhouse gas emissions. This takes place through emissions trading schemes in Europe and North America. In Australia, the 2007 National Greenhouse Gas and Energy Reporting Act created a national framework for reporting emissions and obligated many companies to report. In each case, with slight differences, all affected companies need to collect and report similar data. With a growing international emphasis on greenhouse gas control, reporting requirements can only increase, while cap-and-trade schemes are liable to generate a strong market need for extreme accuracy.

In addition to legislatively driven data collection, some aspects of environmental data management are directly driven by immediate business performance. Many companies will instigate an environmental management system (EMS) to improve environmental performance and reduce costs. Examples include a reduction in energy use, or reduction of waste production or controlling water use. Other aspects will have a less tangible benefit, such as improved compliance and better relationships with regulators, but will nevertheless deliver a business benefit.

For many companies, an EMS will also dovetail into the broader process of sustainability and corporate social responsibility. This process has multiple definitions but, broadly speaking, centers on development that “meets the needs of the present [generation] without compromising the needs of future generations to meet their own needs.” Sustainability is broader than environmental data management and a company needs to develop its own key performance indicators. There are various guidelines as to how these should be developed, but in each case, companies need to collect and report on data.

All of this means people and companies need to collect, manage and report more data than ever before, often more than adhoc systems were ever meant to handle. Some of this comes from monitoring carried out by individuals, contractors and analytical laboratories. There have also been many advances in monitoring and data logging, but often the analysis and reporting of the data is carried out in Microsoft Excel or other basic software packages. This brings a whole range of problems, such as those for the vice president noted in the sidebar. As the data volume grows:

• How do managers know they are on top of all their monitoring and data collection?
• How can users more easily judge performance and compliance (e.g. with permit threshold)?
• How can data be shared?
• How can organizations regularly and efficiently produce their reports for internal or external bodies?
• How can users view historical trends in data over many years that are spread among a host of different systems or platforms?
• Can users defend their data and audit it?
• How much time does all this take?
• How many errors are in the system and can data be validated?

Many clients spend a good deal of money on data collection and collation – up to $350,000 at a single site – and yet they are not able to capture the full value. This means that the staff is spending work time inefficiently crunching numbers. Turnover at such positions further creates problems as additional training is required. Reporting is often late and the system becomes error-prone, damaging relationships with regulators and other stakeholders.