GCRI · Advanced Industry and Robotics

Environmental engineering for industry

Environmental engineering controls industry’s emissions, effluents and waste and designs circular material flows. In this field it covers pollution control technologies, wastewater treatment, waste management and environmental monitoring around industrial sites.

01Register

Environmental engineering for industry on the record.

Method recordSTEM method
  1. MethodWater and environmental engineering
  2. Applied inAdvanced Industry and Robotics
  3. Canonical pageSTEM Commons
02Application

What water and environmental engineering does here.

Water and environmental engineering designs systems for water supply, wastewater treatment, drainage and pollution control. It protects public health and ecosystems while meeting the demands of cities, industry and agriculture. In industry and robotics, work and skills and trade, resources and supply chains, environmental engineering connects hydrological hazards to service continuity and public health. It informs investment in water infrastructure that supports food, energy and urban resilience. The canonical description is on the STEM Commons.

Emission control selection

Abatement technologies for air pollutants are compared against best available techniques and permit limits for a given process.

Industrial wastewater treatment

Treatment trains are designed for the contaminants in process effluents, with monitoring to verify discharge quality.

Community exposure monitoring

Air and water quality around industrial sites is monitored to assess exposure of nearby communities.

03Geography

Water now in Germany.

Rain, flood water and night lights over Germany, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Water now, for this page

About this layer
Evidence
Estimated
Source
GPM IMERG (NASA GES DISC)
Provider
NASA GES DISC
Licence
NASA open data
Vintage
daily; Satellite estimate, near real time
Newest at build
2026-10-12
Resolution
about 2 km grid
Uncertainty
Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed (clouds leave gaps)
Source
VIIRS Global Flood Product (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite detection; clouds leave gaps
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
Provider
NASA LAADS DAAC
Licence
NASA open data
Vintage
daily; Satellite radiance, corrected for moonlight and atmosphere
Newest at build
2026-10-12
Resolution
about 500 m grid
Uncertainty
Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Modelled
Source
SMAP Level 4 (NASA NSIDC DAAC)
Provider
NASA NSIDC DAAC
Licence
NASA open data
Vintage
daily; Model with satellite data assimilated
Newest at build
2026-10-11
Resolution
about 2 km grid
Uncertainty
Model with satellite data assimilated. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Estimated
Source
Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
Provider
NASA SEDAC (CIESIN, Columbia University)
Licence
CC BY 4.0
Vintage
null; Census-based estimate, static (2020)
Resolution
about 1 km grid
Uncertainty
Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
Evidence
Observed
Source
VIIRS NOAA-20 corrected reflectance (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite image
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
Reached through
gibs.earthdata.nasa.gov, only after you allow the live map

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Water now, framed on Germany.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA GPM IMERG Early · NASA JPL OPERA DIST-ALERTSources and licences

  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • SMAP Level 4 soil moisture and snow mass, NASA Global Modeling and Assimilation Office; distributed by the NASA National Snow and Ice Data Center DAAC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Live: NASA GPM IMERG Early. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
  • River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
  • Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
  • Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (14 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Root-zone soil moisture, 0 to 100 cm: SMAP Level 4, NASA NSIDC DAAC · 11 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026

04Data

What the method consumes.

Datasets and records used by water and environmental engineering on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DataRoleLabel
Emission monitoring dataComplianceOBSERVED
Effluent samplingTreatment performanceOBSERVED
Ambient monitoringExposureOBSERVED
05Monitor

Field sampling.

The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.

Field sampling

Industry and robotics · Environmental engineering · Run R-2372

Three sites overdueGCRI · Advanced Industry and Robotics

Sampling sites

red: result above guideline

Circle size: exposure value · exampleN

Latest results

S-32Overall3.88Below
S-50Robot11.57Below
S-37Energy7.88Above
S-40Waste5.27Below

Trend at selected site

Field sampling: the screen’s structure, terms and states for Industry and robotics.Example names and illustrative values; a design, not a record or a live system.
06Validation

How results are checked.

Designs are verified against regulatory standards and pilot testing. Network models are calibrated with field pressure and flow measurements.

CheckHow it is done
Monitoring quality assuranceMonitoring follows standard methods with quality control
Permit comparisonResults are compared with permit limits
Independent verificationCommunity monitoring is reviewed independently of the operator

Ageing assets and incomplete records limit model accuracy. Emerging contaminants and changing rainfall patterns challenge existing designs.

07Simulator

A shock through an input-output economy.

A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows Germany; the model itself is not tied to a place.

Controls

Sector hit first (position in the list)
0
Size of the shock
30% of its output
Duration of the shock
30 days
Recovery rate
0.08 per day
Days simulated
180 days

Readouts

Output lost over the run
0.84% of a year’s output
Total over direct (multiplier)
2.01 times
Most affected other sector
Trade
Its peak inoperability
5.5%
Numbers, methods and sources
SectorPeak inoperability (%)Output weight (%)
Agriculture31.6617.7
Energy0.5419.7
Manufacturing3.3117.4
Transport and ports3.0013.6
Trade5.5415.1
Services0.6216.5

Methods

  1. Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
  2. Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
  3. Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:

  • Seaports Natural Earth
  • Maritime chokepoints Nexus (named straits and canals at their published coordinates)
  • Railways Natural Earth
  • Major roads Natural Earth
A shock through an input-output economy. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Input-output analysis (Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press); Inoperability input-output model (Haimes and Jiang (2001), Journal of Infrastructure Systems 7); Dynamic inoperability and recovery (Lian and Haimes (2006), Systems Engineering 9).
09Twin and cascade simulator

Model environmental engineering for industry as a system. Run the cascade.

Assembled from the Nexus Standards area profile Industry and manufacturing: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.

Hazards

  • Natech event
  • Dam failure
  • River flood (set off downstream)
  • Flash flood (set off downstream)
  • Mass displacement (set off downstream)
  • Chemical spill or leak (set off downstream)
  • Emergency-response overload (set off downstream)
  • Widespread power outage (set off downstream)
  • Water supply shortfall (set off downstream)
  • Toxic gas release (set off downstream)
  • Industrial fire (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NS-12 Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)
  • NRS-H60 Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
  • NRS-H50 Hazard family: Industrial and process accidents (NHS-1 infra.industrial)
  • NRS-H109 Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)
  • NRS-X10 Chemical, industrial, nuclear and radiological safety
  • NRS-H54 Hazard family: Signal and platform disruption (NHS-1 cyber.platform)
  • NRS-K14 Domain pack profile: Manufacturing industrial (DOMAIN-014)

Observation

  • eMARS Major Accident Reporting System (listed for reference)
  • World Development Indicators (listed for reference)
  • ILOSTAT (listed for reference)
  • Manufacturing value added as a proportion of GDP and per capita
  • CO2 emission per unit of value added
  • Manufacturing, value added (% of GDP)
  • Direct economic loss to all other damaged or destroyed productive assets attributed to disasters

Example geography

  • The Strait of Malacca: 227 hexagonal cells
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Surface soil moisture (SMAP), NASA GIBS
10Stewardship

Who maintains the method.

Water Nexus stewards water and environmental engineering for the Consortium and reviews how it is applied across platforms.

11Methods and evidence

How environmental engineering for industry is modelled, measured and tested.

The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.

System model

The couplings between the systems in environmental engineering for industry: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−++IndustryWorkTradeCommunitiesEconomyR reinforcingB balancing
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
13Questions

Questions and answers.

What are the limits of water and environmental engineering in this field?
Ageing assets and incomplete records limit model accuracy. Emerging contaminants and changing rainfall patterns challenge existing designs.
How are results checked?
Designs are verified against regulatory standards and pilot testing. Network models are calibrated with field pressure and flow measurements.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute

Contribute evidence from this page.

Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIAdvanced Industry and RoboticsExponential TechnologyGlobal Sector Risk and Resilience
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Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.