Waste Services — Germany
Across five Waste Services refuse collection vehicles running their regular rounds with their regular drivers, fuel consumption over 16 weeks (15 June to 30 September 2026, 296 vehicle-days) averaged 17.7% below the same vehicles’ own March–May baseline — measured from Waste Services’ own fleet management system, with the drivers not informed that the treatment had been applied. The vehicles also ran lighter and in a warmer season than in the baseline; both effects are set out on this page.

| Operator | [operator address redacted] |
|---|---|
| Vehicles | 5 refuse collection vehicles (3× Iveco, 1× Volvo, 1× Mercedes), 7.2 t to 18 t bodies |
| Baseline | March, April and May 2026 — same vehicles, same depot, before any treatment |
| Trial window | 15 June to 30 September 2026, 16 weeks and 296 vehicle-days (trial still running; October follows) |
| Measured quantity | Average daily consumption in l/100 km |
| Data source | Waste Services’ own fleet management system plus daily operational records (tour, hours, distance, load) supplied by Waste Services; temperatures from the German Weather Service |
| Route continuity | 83% of July journeys and 71% of journeys from July to September ran to destinations that were also served during the baseline |
| Load | Average utilisation fell from 0.87 to 0.79 between the two periods — see the second chart |
| Temperature | Regional mean 10.5 °C in the baseline months and 18.9 °C across the trial window (German Weather Service) |
| Blinding | Drivers were not informed that the air filters had been treated |
| Control group | None — comparison is against the vehicles’ own earlier baseline |
Every measurement, not just the best one
| Vehicle | Make | Baseline (l/100 km) | Trial (l/100 km) | Change |
|---|---|---|---|---|
| Vehicle 01 | Iveco | 25.70 | 20.61 | −19.8% |
| Vehicle 02 | Iveco | 22.22 | 20.57 | −7.4% |
| Vehicle 03 | Iveco | 24.21 | 16.35 | −32.5% |
| Vehicle 04 | Volvo | 23.35 | 19.79 | −15.3% |
| Vehicle 05 | Mercedes | 24.13 | 20.84 | −13.6% |
| Fleet | −17.7% (average of the five changes) |
Five small line charts, one per vehicle, on a shared scale from zero to 28 litres per 100 km. The grey line on the left is the measured monthly consumption in March, April and May, before the treatment was applied. The green line on the right is the weekly average during the trial, weeks 1 to 16. The dashed line is the March–May mean that each result is calculated against. Every vehicle runs below its own pre-treatment level in every recorded trial week; gaps are weeks without a reading.




These are the air filters the treatment was applied to, photographed by us on site at the North Rhine-Westphalia depot. Four photographs cover the five vehicles because two of them — Vehicle 01 and Vehicle 02 — are the same Iveco with the same 12 t box body and therefore the same filter; the operational records list them under the identical body class. The other three vehicles each run a different element, which is why no two of these photographs look alike. Together they document what was treated: standard OEM filters, in a working depot, not a laboratory. They are not a before-and-after comparison — the record carries no date and no application status for any of them, so nothing about the condition of a filter in these photographs should be read as a result.
Why we anonymise
Customer confidentiality is part of our professional standard.
We value the confidentiality of our customers, partners and data owners. Publicly presented test cases are therefore anonymised and do not disclose vehicle identities, vessel names, fleet details or commercially sensitive operating data unless explicit public disclosure has been agreed.
Our principle
Proof should come from validation, not from borrowed credibility.
Our technology should be judged by a transparent test approach and by each customer’s ability to verify NanoEFX under their own operating conditions — not by a wall of past customer logos.
Method before result
The five vehicles ran their normal collection rounds throughout, out of the Waste Services depot in North Rhine-Westphalia — same vehicles, same depot, regular drivers. Fuel consumption came from Waste Services’ own fleet management system rather than from any measurement we installed, and Waste Services verified the figures as the owner and operator of the vehicles.
The drivers were deliberately not told that the air filters had been treated. That matters more than it sounds: if a driver knows a fuel-saving product is being tested, driving style tends to change on its own. Keeping the drivers uninformed reduces that bias in the result — it cannot rule out every difference in driving between spring and summer.
The headline figure is the average of the five vehicles’ changes, each calculated from the vehicle’s daily consumption in litres per 100 km against its own March–May average (296 vehicle-days). No distance enters it, which matters because the distance records have gaps (see the limits). Weighting the five changes by recorded kilometres would give a somewhat larger figure, about 19.7%; we quote the unweighted one because the distances behind the weights are incomplete.
Waste Services additionally supplied the daily operational records for all seven months, March to September — destination, hours, distance and load for every working day. That second dataset is the reason this page says more about the trial’s weaknesses than the fuel figures alone could. It shows that the recorded distance totals 43,998 km between 12 June and 30 September, it establishes how far the routes stayed the same, and it uncovered the load shift described below. The load figures are recalculated from the same records with the unit weights Waste Services applies in its own evaluation, 90 kg per tonnage unit and 20 kg per further moved unit — assumptions of the operator that we have not verified.
Route continuity was checked destination by destination. In July, 83% of the journeys went to destinations that also appear in the March–May records; across July to September the share is 71%, and for individual vehicles it ranges from 40% to 75%. The June sheet carries no destination column, so June could not be checked. The routes therefore stayed largely, not entirely, the same: the objection "they were driving different routes" can be answered with data, and the answer is "partly".
Temperatures come from an independent source, the German Weather Service: the regional mean for North Rhine-Westphalia was 10.5 °C across the baseline months March to May and 18.9 °C across the trial window, weighted by the vehicle-days in each month — 8.4 °C warmer. On the five vehicles’ own baseline months the temperature response is weak: −0.12 litres per 100 km per °C (t = −1.2, 15 vehicle-months). Taken at that estimate, about 4 of the 17.7 percentage points could be seasonal.
The limits of this trial
We publish these next to the result rather than in a footnote. If a limitation would change how you read the number, you should read it at the same time as the number.
Known limitations
- There is no control group. Untreated sister vehicles running the same routes over the same weeks would separate the treatment effect from the season and from the load shift in one step — this is the single biggest improvement available for this trial.
- The vehicles ran lighter during the trial. Average capacity utilisation across the fleet fell from 0.87 to 0.79 — down 8.7% — and distance per driving day fell 6.6%. A lighter refuse truck burns less fuel per 100 km regardless of any treatment, so some unknown part of the 17.7% is a load effect rather than a product effect. This is the most important caveat on this page, and we would not know about it if Waste Services had sent only the fuel figures.
- Against that: the load shift does not track the result. The change in utilisation and the saving across the five vehicles correlate at +0.01 — effectively none — and Vehicle 04 carried 7.0% more utilisation while using 15.3% less fuel. Within the same vehicle, daily load and daily consumption showed no detectable relationship (+0.02 l/100 km per tonne, t = 0.8, 249 vehicle-days). That is the absence of a detectable effect, not proof that there is none: the unit weights behind the load figures are assumptions, and with five vehicles this is an argument, not a proof.
- The baseline is seasonal. March–May, at 10.5 °C, is compared with a window that averaged 18.9 °C, and summer diesel, fewer cold starts, no cabin heating and drier roads all reduce consumption on their own. The temperature response measured on the vehicles’ own baseline months is weak, so the seasonal share could be smaller or larger than the roughly 4 percentage points it implies.
- Route continuity fell over the summer. 83% of the July journeys went to baseline destinations, but only 71% across July to September, and as little as 40% for one vehicle (Vehicle 02). A changed round can change consumption in either direction; we have not separated that effect.
- The distance records are incomplete. On 47 working days between 12 June and 30 September a vehicle moved units and logged hours, but no usable kilometre reading was recorded — 19 read “no data”, 9 are unclear and 19 read 0. Roughly 9,000 km, about 17% of the distance covered, is missing, and one 248 km tour is dated to a day that does not exist. This is why distance does not enter the headline figure; litres per 100 km are recorded for every vehicle-day used.
- Vehicles dropped out. Vehicle 02 left the trial — last reading 21 August, lease ended 1 September — so the fleet average rests on at most four vehicles from week 11. Vehicle 01 was out of service in weeks 6 and 7 (an entry “water in oil” is recorded on 21 July) and again around 24 August; Vehicle 03 was in the workshop on five days in early September.
- Planned services fall inside the trial. The vehicle records list service intervals, and for four of the five vehicles a planned service is due within the trial window — Vehicle 04 in July, Vehicles 01, 02 and 03 in September. According to the operator, the air filters were not replaced, cleaned or treated again: the mileage did not yet require it, and an inspection found them clean enough.
- Traffic, driver rotation and vehicle maintenance status were not systematically controlled during this proof-of-concept phase.
- The spread between vehicles is wide — from 7.4% to 32.5%, and 14.0% on average without the strongest vehicle. Route changes do not explain it: Vehicle 03, the −32.5% vehicle, has the highest route continuity of all five at 75%. It remains an outlier to be explained rather than a headline.
- The data are documented — daily consumption data, daily operational records and the operator’s fleet-management system. The trial report itself calls them proof-of-concept data and names a controlled validation trial with a defined protocol and a control group as the next step towards certified documentation. The trial is still running; October follows once its operational records are available.
Reading this evidence in context
For a municipal waste operator, refuse collection is a high-consumption, low-speed, stop-start duty cycle, and fuel is a dominant, controllable cost line.
What this trial supports is a clear, consistent direction: all five vehicles came in below their own baseline in every recorded week — 69 vehicle-weeks — on routes that largely stayed the same, with drivers who did not know. What it does not support is a precise number. Two effects push in our favour and are not fully quantified — the season and the lighter loads — and neither can be separated out without a control group.
That is why the figure to take from this page is the direction and the method, not the 17.7%. An operator planning a budget should assume less than this trial shows, and then measure their own fleet.
The honest next step is the one the report itself recommends, and the operational records now make it easy to design: a controlled validation trial with untreated sister vehicles on the same rounds, and load recorded per day so it can be held constant in the analysis rather than discovered afterwards.
Questions operators ask about this trial
Is the 17.7% a measured or a calculated figure?
Why were the drivers not told about the treatment?
How is the fleet figure calculated?
The trucks ran lighter during the trial. Does that not explain the whole saving?
Was it not simply warmer than in the baseline months?
How stable were the routes?
Can we see the underlying data?
Does this prove NanoEFX saves 17% on any fleet?
Provenance
- Operator
- [operator address redacted] — all journeys start and end here
- Data owner
- Waste Services (own fleet management system and operational records), figures verified by the operator
- Documents
- NanoEFX Proof of Concept Report, Waste Services Germany trial (confidential); daily consumption records, 15 June to 16 October 2026 (litres per 100 km per vehicle and working day); daily operational records for March to September 2026 (destination, hours, distance, units moved); vehicle master data and monthly averages; German Weather Service (DWD) regional mean air temperature for North Rhine-Westphalia, monthly, March to September 2026
- Photographs
- Taken on site at the North Rhine-Westphalia depot by ECO EFX Solutions GmbH, filed against each vehicle in the trial record — our own images
- Baseline period
- 1 March to 31 May 2026
- Trial window
- 15 June to 30 September 2026 — 16 weeks, 296 vehicle-days; consumption records run to 16 October, the October operational records follow
- Recalculation
- Distance, load and route continuity recomputed from the operational records; monthly total and average rows excluded; consumption, load and weather figures reproduced by script from the source files
- Open items
- What else was done at the planned services; the October operational records; the operator’s own load and weather evaluation, which we recalculated rather than adopted
- Status
- Trial ongoing — figures on this page cover 15 June to 30 September
- Last checked
- 8 November 2026
The only baseline that matters is yours
Every trial on this site was run on someone else’s vehicles, duty cycle and baseline. A NanoEFX pilot measures yours — and we will help you design it with a control group so the result stands up.
