#environmentalmonitoring — Public Fediverse posts

Managing Facility-Induced Food Waste | Food Engineering

A 2024 UN document titled “Think – Eat – Save: Tracking Progress to Halve Global Food Waste” stated that food waste is a market failure that results in the throwing away of more than $1 trillion’s worth o…
#dining #cooking #diet #food #Food #Building(Construction) #Cross-contamination #drains #environmentalmonitoring #foodlossandwaste #foodwaste
https://www.diningandcooking.com/2775024/managing-facility-induced-food-waste-food-engineering/

Managing Facility-Induced Food Waste | Food Engineering https://www.diningandcooking.com/2775024/managing-facility-induced-food-waste-food-engineering/ #Building(Construction) #CrossContamination #drains #EnvironmentalMonitoring #food #FoodLossAndWaste #FoodWaste

Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
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https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
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https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
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H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
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“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal

Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
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https://doi.org/10.3390/geosciences15030110 <-- shared paper
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H/T @Geosciences MDPI
“This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
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“Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
#FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement

Can industrial emission monitoring become more proactive? 🌍

A Continuous Emission Monitoring System (CEMS) provides real-time emission data, while AI-powered analytics can help organizations gain deeper operational insights from historical and live data.

Learn about CEMS:
https://scada-thai.com/products/continuous-emission-monitoring-system

Explore AI Predictor:
https://scada-thai.com/products/ai-predictor

#CEMS #ATSCADA #AI #EnvironmentalMonitoring #Industry40

Environment Auckland [New Zealand] Data Portal [incl. spatial]
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https://environmentauckland.org.nz/Data <-- shared link to data portal
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https://ourauckland.aucklandcouncil.govt.nz/news/2025/09/soe-report-2025/ <-- shared 2025 report link
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https://coastalmonitoringac.netlify.app/ <-- shared Auckland Council Beach Monitoring Program page
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[ancetodal: a VERY long time ago I was an intern Engineering Geologist at the Auckland Council, although it was ARC back then 😊 ]
H/T @David Wright | Senior Field Hydrologist, Hydrology And Data Management Team
“This portal contains primary data from Auckland Council’s State of the Environment monitoring programmes.
Te Kaunihera o Tāmaki Makaurau / Auckland Council's Environmental Evaluation and Monitoring Unit carries out environmental monitoring across the region. [They] have been collecting information about Auckland’s environment for more than 30 years and have more than 1,000 monitoring sites across the region. [Their] comprehensive monitoring programmes build a picture of the health of Auckland’s environment, track changes and identify issues...”
#opendata #Auckland #NewZealand #GIS #spatial #mapping #dataportal #localgovernment #publicservice #publicgood #ratepayers #StateoftheEnvironment #monitoringprogrammes #environment #water #hydrology #waterquality #waterresources #intergration #environmentalmonitoring #airquality #coast #coastal #development #construction #engineering #sediment #biology #biodiversity #ecology #habitat #monitoring #spatialanalysis #spatiotemporal #estuary #river #stream #marine #mitigation #identification
@Auckland Council

Warsaw’s Water Quality Secret: Meet the Clams on Duty

Dear Cherubs, imagine trusting your city’s drinking water to a creature with no brain, no Wi-Fi, and zero interest in your opinions. In Warsaw, that’s not a joke—it’s infrastructure.

The Polish capital, home to nearly two million people, runs a 24/7 water monitoring system that relies on clams—specifically freshwater mussels—to act as living alarm systems. It sounds like a quirky science fair project, but it’s very real, and, frankly, kind of genius.

HOW THE CLAMS CLOCK IN

Here’s the deal: mussels naturally filter water and react quickly to changes in its quality. When something’s off—pollution, toxins, anything sketchy—they clamp shut. Hard stop.

According to reports from Warsaw’s Municipal Water and Sewerage Company, sensors are attached to the shells of these mussels, tracking how wide they’re open in real time. When several clams close simultaneously, the system flags it as a potential contamination event. Translation: the clams are basically unionized quality inspectors who don’t miss a shift.

And yes, it’s automated. The shell movements are monitored digitally, feeding data into the city’s control systems. No lab coat required—just a few dozen quietly judgmental mollusks doing their thing.

WHY THIS ISN’T AS RANDOM AS IT SOUNDS

If this feels a bit “is this giving medieval vibes?”—fair. But it’s actually backed by solid biology.

Freshwater mussels are extremely sensitive to pollutants. According to environmental research cited by outlets like the BBC, they respond faster than many mechanical sensors to certain contaminants. While a machine might need calibration or maintenance, a mussel just… reacts.

Also, they don’t fake it. No false positives because someone forgot to update firmware. If a clam snaps shut, something’s up.

That said, the system isn’t replacing modern testing. It complements it. Think of the mussels as an early warning system—like the canary in the coal mine, but with better PR and less existential dread.

LOW-KEY ECO-TECH FLEX

There’s something quietly brilliant about combining biology with technology instead of trying to out-engineer nature entirely. Warsaw’s setup is a reminder that innovation doesn’t always mean more complexity—it sometimes means paying attention to what already works.

According to thisclaimer.com, hybrid systems like this—where natural processes are integrated into modern infrastructure—are gaining traction globally as cities look for resilient, low-energy monitoring solutions. It’s sustainable, cost-effective, and, let’s be honest, a great conversation starter.

Also worth noting: the mussels are not harmed in the process. They’re rotated and cared for, because even the best employees deserve decent working conditions.

So next time you pour a glass of tap water in Warsaw, just know a team of silent, shell-based professionals has already vetted it. No app, no alert—just vibes. Good ones.

Sources list:
BBC — https://www.bbc.com/news/world-europe-15977152
Reuters — https://www.reuters.com/article/us-poland-water-clams-idUSTRE79Q3QZ20111027
Municipal Water and Sewerage Company in Warsaw — https://www.mpwik.com.pl
thisclaimer.com — https://thisclaimer.com

Just came across the UNSCEAR 2024 report — and the questionnaire topics covered are a genuinely interesting read for anyone following radiation science or environmental monitoring.
Page 12 in particular gives a clear picture of what the international radiation protection community is actively assessing. Well worth a look if this is your field.
🔗 https://www.unscear.org/unscear/en/publications/2024_2.html
#UNSCEAR #RadiationScience #EnvironmentalMonitoring #OpenScience #PublicHealth

https://winbuzzer.com/2026/05/12/georgia-data-center-secretly-used-29m-gallons-of-water-xcxwbn/

Fayette County, Georgia asked residents to stop watering lawns during drought while a 6.2 million-square-foot QTS data-center campus moved water through two unmetered pipes and faced no fine beyond a $147,474 retroactive bill.

#DataCenters #Sustainability #Environment #EnvironmentalMonitoring #Georgia #QualityTechnologyServices

Happy 1st of May!
16 sensors, 16 very different workplaces – but behind every single one: a person.

A researcher. An engineer. A technician. Someone in a city team, a utility, on a farm. People whose work turns sensor readings into better decisions for our environment.

This one's for them.

P.S. Got a Decentlab sensor of your own? Show us – we're collecting pictures for our next collage. 📸

#IoT #LoRaWAN #EnvironmentalMonitoring

China launches PRSC-EO3 for Pakistan, lofts internet test and environment monitoring satellites

https://fed.brid.gy/r/https://spacenews.com/china-launches-prsc-eo3-for-pakistan-lofts-internet-test-and-environment-monitoring-satellites/

We breathe over 10,000 liters of air every day – with no idea what's in it.
Our DL-PM sensor has been measuring particulate matter continuously for five years. The data shows winter inversions, New Year's Eve spikes, and that air quality has no quiet season.
The patterns only emerge when you measure continuously.

👉 Check real-time demo: https://demo.decentlab.com/d/DL-PM/dl-pm-particulate-matter-temperature-humidity-and-barometric-pressure-sensor-for-lorawan-r?orgId=1&refresh=15m

#ParticulateMatter #LoRaWAN #EnvironmentalMonitoring #AirPollution

Getting started guides are now live at docs.wesense.earth.
Choosing sensors, building a node, Meshtastic integration, Home Assistant/Ecowitt etc.
Includes our durability-over-accuracy sensor philosophy, full MQTT command reference, and datasheets for all 26 supported sensors.
Consider this draft, but there’s enough to get started contributing to the world's first free, community-owned environmental sensor network. #WeSense #OpenSource #IoT #EnvironmentalMonitoring #Meshtastic #LoRaWAN

This is early days. We're looking for people who want to place sensors in their homes, streets, and communities.

Follow along here, or visit wesense.earth to learn more.

#OpenScience #Environment #OpenHardware #AirQuality #CitizenScience #Decentralised #Meshtastic #IoT #WeSense #NewZealand #Aotearoa #NZ #Auckland #AirQuality #Homelab #EnvironmentalMonitoring

What we measure: air quality, PM2.5, CO2, temperature, humidity, and more — reported every 5 minutes to a global P2P network.

No central server owns the data. If we disappeared tomorrow, the network and data survive. #OpenScience #Environment #OpenHardware #AirQuality #CitizenScience #Decentralised #Meshtastic #IoT #WeSense #NewZealand #Aotearoa #NZ #Auckland #AirQuality #Homelab #EnvironmentalMonitoring

How did Calgary respond to the wet summer of 2025?
Here’s the median summer NDVI map derived from Sentinel-2 imagery.
You can clearly see the Bow River corridor, Nose Hill Park, and the contrast between established tree-rich communities and newer developments.

Full analysis here:
https://www.datastory.org.ua/how-much-greener-is-calgary-in-2025/

#Geospatial #NDVI #UrbanClimate #Calgary #RStats #GreennessOfCalgary #yyc #Apberta #QGIS #foss4g #EnvironmentalMonitoring #UrbanHealth

Analysis of seawater for DNA of rare and endangered hammerhead sharks has been used to infer their presence or absence at multiple locations throughout their range. No sharks were captured or observed; accuracy of the inferred distributions depends on the presumed degradation of environmental DNA in hours or days.

Summary: https://scitechdaily.com/scientists-find-endangered-hammerhead-sharks-without-seeing-a-single-one/

Original paper: https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1688088/full

#Science #MarineScience #EnvironmentalMonitoring #EnvironmentalDNA #Hammerheads

Environmental Monitoring on the Cheap

https://web.brid.gy/r/https://hackaday.com/2026/01/24/environmental-monitoring-on-the-cheap/

Detecting Drought Stress In Trees From The Air [Switzerland]
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https://www.linkedin.com/pulse/detecting-drought-stress-trees-from-air-eidg-forschungsanstalt-wsl-mrjue <-- shared technical article
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https://doi.org/10.1016/j.agrformet.2025.110856 <-- shared paper
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https://www.inverse.com/science/trees-dying-shocking-rates-understanding-save-them <-- shared technical article
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#Umweltbeobachtung #Wald #Trockenheit #Drohnen #Forschung #EnvironmentalMonitoring #Forest #Trees #Drought #Drones #Research #UAV #GIS #spatial #mapping #remotesensing #tree #vegetation #climatechange #heatstress #hot #summer #sunlight #discolouration #leaf #leaves #defoliation #impacts #crowns #canopy #water #hydrology #drought #risk #hazard #drone #imagery #survey #nativetree #monitoring #spatialanalysis #spatiotemporal #measurement #methodology #drought #coping #Droughtstress #Forest #NDVI #PRI #Xanthophyll #Turgorlosspoint #waterstress #dehydration #mortality #death #dying #loss #hydraulicfailure #biology #botany #canopy

The Future of Pollution Monitoring Will Be Seen From Above
Plastic waste and chemical pollution don’t stay in one place.
know more:https://zurl.co/D3EP3#AIVision
#AIVision #PollutionTracking #EnvironmentalMonitoring #DroneImaging #AerialVision #PlasticPollution #ChemicalDetection

Comparison of the median-seasoned NDVI for central Calgary: 2024 vs 2025

Here is a side-by-side look at how the vegetation conditions in central Calgary changed between two years, using median-seasoned NDVI maps derived from Sentinel-2 imagery.

You can clearly see the interannual differences in greenness — especially in parks, riparian zones, and residential areas with large tree cover.
The spatial patterns remain stable, but 2025 shows noticeably higher NDVI in many neighbourhoods due to more favourable moisture conditions.

This is part of my ongoing project on analyzing the vegetation dynamics of Calgary communities: #GreennessOfCalgary

#RemoteSensing #EarthObservation #NDVI #Sentinel2 #Calgary #UrbanEcology #GIS #RStats #DataAnalysis #EnvironmentalMonitoring #Copernicus #Alberta #Canada #YYC #UrbanHealth #QGIS #FOSS

In the distant 2016, when I was a member of an environmental NGO in Kryvyi Rih (Ukraine), I started using satellite Earth Observation data to monitor the condition of large industrial tailings ponds.

At that time, environmental regulations required these storage facilities to be either flooded or at least kept moist to prevent dust storms.
Industrial operators often ignored these rules, leaving huge dry surfaces exposed — which created massive dust pollution affecting nearby communities.

Using Sentinel-2 and Landsat-8 imagery with false-color composites, I developed a simple but effective method to map dry, moist, and water-covered zones of tailings ponds.

Local residents and journalists were absolutely delighted! Industrial companies, on the other hand… reacted very differently 🤣

These maps are from 2016–2017 and show several tailings facilities around #KryvyiRih.

#RemoteSensing #EarthObservation #Sentinel2 #OpenData #EnvironmentalMonitoring
#Tailings #Mining #DustPollution #GIS #QGIS #Ukraine #Landsat

🌿 Greenness of Calgary Communities (Summer 2024)
📎 https://www.datastory.org.ua/greenness-of-calgary-communities-summer-2024/

Last year I published my first attempt to analyze the actual vegetation condition across Calgary and to build a data-driven ranking of its communities based on median summer NDVI. It was my very first experiment in assessing urban greenness at the neighbourhood scale — but the results turned out surprisingly insightful.
Some patterns were expected, while others revealed unexpectedly low vegetation density in places that looked green from the ground.

This exploration later grew into a much larger line of research on Calgary’s greenness, climate resilience, and spatial variability in vegetation health. You can find some results here with thematic hashtag #GreennessOfCalgary

If you're working on urban ecology, remote sensing, or land-cover analysis of Canadian cities — I’d be happy to exchange ideas.

#NDVI #RemoteSensing #Calgary #UrbanEcology #Sentinel2 #QGIS #RStats #Alberta #Canada #EnvironmentalMonitoring #GeospatialAnalysis