Air Pollution Solutions
Know the number. Know where it is going. Know who is causing it.
Ambient readings are the input, not the product. The platform projects the trace forward with a stated confidence, attributes the loading to the activities driving it, and maps each threshold crossing onto a defined operational response.
Live air quality, six hours ahead, with the source attributed.
Ambient readings are only the beginning. The platform projects where the trace is going, states its confidence, and apportions the loading between the activities actually driving it.
Live Air Quality
Updated 4 minutes ago
Dominant pollutant is PM2.5 at 82 µg/m³, which is 37% above the 24-hour standard.
- PM2.5+6.4%
0µg/m³
137% of standard
- PM10+4.1%
0µg/m³
146% of standard
- NO₂-1.8%
0µg/m³
68% of standard
- SO₂+0.6%
0µg/m³
26% of standard
- O₃-2.2%
0µg/m³
39% of standard
- CO+0.3%
0.0mg/m³
70% of standard
24-Hour Trace & AI Forecast
Observed readings with a six-hour projection
- Observed
- Forecast
Air quality index over the last 24 hours, rising from 121 at midnight to a peak of 205 at 8 PM and currently 187.
AI forecast projects the air quality index rising from 142 at noon to 208 by 8 PM, with 87% confidence.
Pollution risk increasing — projected AQI 208 by 8 PM
Particulate loading is projected to rise through the evening as the boundary layer collapses and wind speed drops below 1.2 m/s. Model confidence 87%.
AI Source Detection
Estimated contribution to current particulate loading
Estimated pollution source split: industrial zone 42 percent, traffic 31 percent, construction 17 percent, other 10 percent.
How attribution works
Source apportionment combines pollutant ratios, temporal signatures and wind-field geometry. Each category carries the evidence that placed it there, so an inspector can challenge the conclusion rather than accept a number.
Select a source to see the supporting signals.
Attribution is what makes a reading actionable.
An exceedance with no attributed cause produces a report. An exceedance traced to a category, with the signals that placed it there, produces an inspection.
Attribution
Demo / Illustrative DataSelect a source to isolate its connection to the hotspot and read the signal that supports it.
A number tells you where you are. A trajectory tells you what to do.
Heterogeneous inputs are normalised and time-aligned, passed through blended short-horizon models, and returned as a risk trajectory with a stated confidence band — then mapped onto a defined operational response.
Data
Heterogeneous inputs are normalised, quality-scored and time-aligned.
- IoT Sensors
- Satellite
- Weather
- Traffic
- Industrial Activity
AI Engine
Short-horizon models are blended and calibrated against recent error.
- Feature synthesis
- Sequence models
- Ensemble blending
Prediction
Output is a trajectory with stated uncertainty, not a single number.
- Risk trajectory
- Confidence band
- Exceedance probability
Action
Each threshold crossing maps to a defined operational response.
- Alert
- Dispatch
- Advisory
- Intervention
Risk Projection
Six-hour horizon · 84% confidence
Current Risk
0Predicted Risk
0+17Horizon
+6h
- Observed
- Predicted
Environmental risk index rises from 51 six hours ago to 64 now, and is projected to reach 81 in six hours with a confidence band from 72 to 90.
- Data
- AI Engine
- Prediction
- Action
What the air module actually does
Continuous ambient monitoring
Reference-grade stations and low-cost nodes ingested through one pipeline, with calibration state and drift tracked per device so a reading's trustworthiness travels with it.
Short-horizon AQI forecasting
Six to seventy-two hour projections blending meteorology, boundary-layer behaviour and activity patterns, published with an explicit confidence band rather than a bare number.
Source apportionment
Pollutant ratios, temporal signatures and wind-field geometry combine into a contribution estimate per category, each carrying the evidence that placed it there.
Exceedance alerting
Threshold crossings separated from sensor drift and weather artefacts, so an alert means something happened rather than something moved.
Hotspot persistence analysis
Rolling-window analysis distinguishes a bad afternoon from a location that has exceeded on twenty-two of the last thirty days and needs structural intervention.
Intervention effectiveness
Before-and-after measurement on dust suppression, corridor retiming and enforcement action, so the next budget cycle has evidence behind it.
Start a conversation about your environmental data.
Tell us what you monitor today, what you are required to report, and where the chain currently breaks. We will come back with what a deployment would look like.
MANTRAMIND
Nature & Science in Harmony
Contact details available on request.