Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers

Callum E. Flowerday; Christopher Kitras; Jaron C. Hansen; Philip Lundrigan; Tu Nguyen

REVIEW 4 major objections 5 minor 29 references

Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Deploying low-cost particulate sensors across Ulaanbaatar gers, this field study concludes that an NGO insulation retrofit lowered indoor PM2.5 by 17.5% in coal-heated gers, by 19.1% in hybrid coal-electric gers, and by 48.9% in fully…

desk verdict Solid calibration and a useful field deployment, but the headline 17.5% insulation benefit is a single-sensor cross-sectional contrast that does not support the causal claim. read the letter →

arxiv 2411.17627 v1 pith:TRDQ7RRF submitted 2024-11-26 physics.ao-ph

Callum E. Flowerday , Philip Lundrigan , Christopher Kitras , Tu Nguyen , Jaron C. Hansen This is my paper · ORCID

classification physics.ao-ph

keywords low-costsensorindoorairqualityPM2.5MongoliangersinsulationretrofitUlaanbaatarparticulatemattermonitoringcalibration

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish whether a relatively cheap building intervention—wrapping a Mongolian ger (the round felt yurt home of most Ulaanbaatar residents) in a radiant barrier with an air gap—improves indoor air quality enough to matter for health. Using custom low-cost sensors that measure fine particulate matter (PM2.5), the study compared uninsulated coal-burning gers with insulated gers that burn coal, use a coal-electric hybrid, or heat and cook with electricity only. Over the winter monitoring period, averaged indoor PM2.5 fell by 17.5% in the insulated coal gers, 19.1% in the hybrid gers, and 48.9% in the electric-only gers relative to the uninsulated control group. Those gains are real but incomplete: even the cleanest group averaged 95.5 µg/m3, about 6.4 times the level recommended by international health guidelines, and nighttime outdoor air was dirtier than indoor air, so opening doors pulled pollution in. The practical stakes are that insulation plus electrification is a measurable step toward safer air in one of the world's most polluted capitals, but surrounding coal use still sets a ceiling on what any single household can achieve.

What carries the argument

The argument is carried by a network of custom low-cost sensors, each built around a Sensirion SPS30 optical particle counter and an SCD30 CO2/temperature/humidity sensor with cellular upload and SD-card backup, at a cost near USD 200 per unit. Because optical particle counters undercount at high concentrations—a 'coincidence' effect where one particle shadows another—the authors calibrated the sensors against a TSI Optical Particle Sizer (model 3300) with a 10:1 diluter, burning anthracite coal in a stove similar to Mongolian stoves and fitting a second-order polynomial transfer function to correct the field data. The comparison design then sorts the gers into four classes (unmodified coal, modified coal, modified hybrid, modified electric-only), averages minute-level data over the deployment, and splits daytime (5 am–8 pm) from nighttime to separate cooking spikes from heating emissions.

What would settle it

Give every ger a sensor and weigh every bag of coal for one winter before and one winter after the insulation retrofit, with door-open events logged; if PM2.5 falls no more than the drop in coal burned would predict, the insulation's direct effect is not established.

Watch

Extended reading notes

Core claim

The paper's central claim is that insulating gers is favorable to indoor air quality despite also reducing ventilation. Averaged over the full study period, PM2.5 was 187.0 µg/m3 in unmodified coal-burning gers, 154.3 µg/m3 in insulated coal-burning gers, 151.4 µg/m3 in insulated hybrid coal-electric gers, and 95.5 µg/m3 in insulated electric-only gers, corresponding to drops of 17.5%, 19.1%, and 48.9% respectively. The authors attribute the reductions to lower coal consumption made possible by better heat retention, and they note that the added insulation can trap smoke for longer, an effect they consider outweighed by the emissions cut. They also report that fully electric gers still sit at 6.4 times the 15 µg/m3 health guideline and propose that smoke infiltrating from neighboring coal-burning gers is the reason. At night, outdoor PM2.5 averaged 229.0 µg/m3, higher than indoor levels in all but the modified coal-burning gers, which the paper reads as evidence that door openings pull in polluted air and cause indoor spikes.

Load-bearing premise

The reductions come from comparing different households (unmodified versus modified gers) rather than measuring the same gers before and after insulation, so any unmeasured difference between households—fuel amount, stove habits, occupancy, door-opening—could produce part or all of the reported drop.

Editorial extensions

If this is right

Insulation alone appears to cut indoor PM2.5 by about a sixth even when coal remains the heat source, a gain worth having for health even though it leaves residents above safe levels.
Pairing insulation with electricity for heating and cooking cut indoor PM2.5 about 2.8 times more than insulation alone (48.9% vs 17.5%), making electrification the strongest measured lever.
Because nighttime outdoor PM2.5 exceeds indoor levels in most gers, opening doors pulls polluted air in; insulation cannot lock out neighborhood pollution.
Residual pollution in fully electric gers points to infiltration from neighboring coal-burning households, so district-wide fuel switching may be needed to reach health guidelines.
The monitoring design—inexpensive sensors calibrated once against a reference—can be replicated to evaluate home-energy interventions in other polluted cities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A fair extension of the single-sensor coal-only result is that the 17.5% estimate is the least secure of the three; a randomized pre/post deployment with fuel diaries would either confirm it or show household differences drove the gap.
The nighttime indoor–outdoor gradient implies a testable prediction: if neighboring gers stop burning coal, indoor PM2.5 in the electric gers should fall even without further household changes.
The calibration correction is static; one could extend the method by modeling relative humidity and sensor aging continuously, which would make long-running low-cost sensor networks more trustworthy for policy.
A cost-effectiveness comparison with mechanical filtration or clean-fuel district heating is a natural next step, since even the best-performing gers remain far above the health guideline.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

I read the Mongolian gers paper with the stress-test notes in hand. My short version: the LCS calibration work is a real strength, the deployment is a genuinely useful field record, but the headline "insulation lowers PM2.5" figure (17.5% for coal-burning gers) is not supported by the design as reported.

What's actually new is the specific NGO insulation retrofit and the comparison across heating types (coal, hybrid, electric) within gers. Prior work had measured PM2.5 in gers and used low-cost sensors indoors, but not this intervention. The calibration against a TSI OPS with a 10:1 diluter, using coal smoke similar to local fuel, is the best part of the paper. The sensor network, with cellular backhaul and local logging, is also a solid engineering contribution. Data are available at a stated URL.

The soft spots are substantial. The central comparison is cross-sectional: no pre-insulation baseline in the modified gers, no randomization, and the control group is a different set of households. Coal type/quantity, stove model, occupancy, and door-opening frequency were not measured. The 17.5% reduction rests on one modified coal-burning ger versus five unmodified ones, and the paper's own nighttime numbers show the modified coal ger slightly above the unmodified (200.5 vs. 198.3 µg/m³), so the claimed benefit is carried entirely by daytime data. Treating hundreds of thousands of 1-minute readings as independent gives standard errors like ±0.5 µg/m³ that cannot represent between-household variability. The "baseline corrected, where necessary" step is underspecified. And Table 1 sums to 30 sensors while the text says 28 were recovered (five unmodified in the text, six in the table). That's a fixable but real inconsistency.

That said, the paper is not overhyped. It explicitly flags the single-sensor limitation and the door-opening bias, and it recommends electricity for cooking/heating as the more effective route. The electric-only result (48.9% lower than unmodified, still 6.4 times the WHO guideline) is more credible, both because it uses more sensors and because it lines up with common sense.

This is worth sending to peer review. A serious referee can ask for a between-group test, a reanalysis clustered by household, a clear explanation of the baseline correction, and a reconciled sensor count. The paper would be better if the conclusions were framed as suggestive rather than demonstrated. For readers working on low-cost sensor deployment or on air quality in ger districts, this is a useful reference, but the reduction percentages should not be quoted as causal effect sizes.

Take it seriously, but ask for revision.

Referee Report

4 major / 5 minor

Summary. The paper reports the deployment of custom low-cost PM2.5 sensors in Mongolian gers in Ulaanbaatar over the winter of 2019–2020, including gers that received an NGO insulation retrofit and unmodified control gers. Sensors were calibrated against a TSI optical particle sizer with a diluter. The authors report mean PM2.5 reductions of 17.5% in insulated coal-burning gers, 19.1% in insulated hybrid coal/electric gers, and 48.9% in insulated electric-only gers relative to unmodified gers, while noting that all groups remain far above WHO guidelines. They also analyze daytime/nighttime patterns and conclude that outdoor PM2.5 enters gers when doors are opened. The paper's central claim is that the insulation retrofit reduces indoor PM2.5 concentrations.

Significance. If the causal claim were supported, the results would provide valuable field evidence that low-cost sensor networks can quantify indoor air quality interventions in low-resource settings, and that insulation plus electrification can reduce PM2.5 exposure in Ulaanbaatar gers. The study has concrete strengths: a low-cost sensor network with cellular data transmission, calibration against a reference optical particle sizer using locally relevant coal smoke, and a clearly stated data availability link. However, the headline reduction rests on a very small, non-randomized cross-sectional comparison, and the reported precision is not appropriate for the study design. As an observational pilot with hypothesis-generating conclusions, the manuscript has merit, but the current phrasing overstates causal support.

major comments (4)

[Section 3.1] The headline claim that insulation reduces PM2.5 by 17.5% is based on a comparison of one modified coal-burning ger against five unmodified gers. There is no pre-insulation baseline in the same gers, no randomization, and no measurement of coal type or quantity, stove model, occupancy, or door-opening frequency. The reported difference of 32.7 µg/m3 therefore cannot be separated from pre-existing household differences. The authors should either reframe this result as an observational, hypothesis-generating finding with explicit caveats, or provide an analysis that accounts for household-level confounding (e.g., mixed-effects models with ger as a random effect and a sensitivity analysis excluding the single modified coal-burning ger).
[Table 1 and Section 2.4] The standard errors and confidence intervals reported in Section 3.1 (e.g., 154.3 ± 0.5 µg/m3 for the modified coal-burning group) are calculated from hundreds of thousands of minute-level averaged data points. Consecutive 1-minute readings within a ger are strongly autocorrelated, so the effective sample size for between-group comparison is at most the number of gers, which is 1 for the modified coal-burning group and 5 or 6 for the unmodified group. No between-group statistical test is reported. The paper should report between-ger variability, use cluster-robust inference or a mixed-effects model, and avoid presenting minute-level standard errors as evidence of precision for the group comparison.
[Section 2.5.2 and Table 1] The data processing step described as 'baseline corrected, where necessary' is underspecified and directly affects all reported concentrations and reduction percentages. The authors need to state which sensors were baseline corrected, what the baseline offset was, how 'necessary' was determined, and how the Grubbs' test outlier removal (Section 2.5.2) influenced the dataset. Without this information, the reproducibility of the main numerical results is not established.
[Section 3.2] The nighttime data contradict the overall favorable conclusion for the modified coal-burning ger: nighttime PM2.5 in the modified coal-burning ger (200.5 ± 0.7 µg/m3) is slightly higher than in the unmodified ger (198.3 ± 1.3 µg/m3). The claimed 17.5% reduction is therefore driven entirely by the daytime window, yet the conclusion 'it does appear to be favorable to insulate the gers' is stated unconditionally. The paper should explicitly condition its recommendation on this daytime/nighttime asymmetry and on the fact that the modified coal-burning comparison is based on a single sensor.

minor comments (5)

[Section 2.2 and Table 1] The number of unmodified gers is given as 5 in Section 2.2 but as 6 in Table 1, and the number of modified hybrid gers is given as 19 in Section 2.2 but as 20 in Table 1; these counts should be reconciled.
[Abstract and full text] The spelling of the city name is inconsistent: the abstract uses 'Ulaanbataar' while the main text uses 'Ulaanbaatar'; standardize to the conventional spelling.
[Section 2.5.2] 'Grubb's Test' should be spelled 'Grubbs' test' for the outlier test.
[Section 4] The phrase 'third-world countries' is dated and imprecise; consider replacing it with a more specific descriptor such as 'low- and middle-income countries'.
[Figures 5–7] The figures do not show any measure of between-ger variability or statistical comparison; adding cluster-level error bars or annotation would help readers assess the strength of the group differences.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported PM2.5 reductions are externally calibrated measurements, not fitted or self-referential outputs.

full rationale

The paper's central comparisons are direct averages of low-cost sensor (LCS) measurements grouped by ger type. The LCS calibration is anchored to an external TSI Optical Particle Sizer reference, and the transfer function is derived from a separate chamber experiment, so the measured PM2.5 values are not fitted outputs of the insulation hypothesis. The 17.5%, 19.1%, and 48.9% reductions are arithmetic differences between group means, not parameters estimated from a model that already assumes insulation works. No step in the derivation chain defines the intervention effect in terms of itself. The unspecified 'baseline corrected, where necessary' step and the aging corrections are data-cleaning procedures; without a specific equation showing that they enforce the reported reduction, they cannot be identified as circular. The cited prior work by the same group supports deployment logistics and LCS methodology, not the insulation conclusion. The single-sensor modified-coal group and the lack of randomization are validity and confounding concerns, but they are not circularity under the stated criteria. Therefore, no significant circularity is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim depends on several measurement assumptions: calibration transfer, control group comparability, outdoor monitor representativeness, sensor placement, and particle mass calculation, plus fitted corrections whose values are not reported. The paper introduces no new theoretical entities, so the burden is in the measurement pipeline rather than in a derivation.

free parameters (4)

LCS calibration polynomial coefficients (second-order) = not specified in paper
Derived from a single barrel calibration run of LCS against TSI OPS and applied to all field data; errors in these coefficients affect every PM2.5 value and all reported reductions. Section 2.5.2.
Aging correction polynomial coefficients = not specified
Second-order polynomial regression used to correct sensor aging after 6 months; coefficients not reported; affects field concentrations. Section 2.5.1.
Baseline correction offsets = not specified
Data were 'baseline corrected, where necessary' before averaging; the magnitude and criteria are not described, and this could alter group differences. Section 3.1, Table 1 caption.
Daytime window (5 am to 8 pm local) = 5 am to 8 pm
Hand-chosen based on visual inspection of cooking peaks in Figure 4 before day and night comparisons; a different window would change the reported daytime and nighttime means.

assumptions (5)

domain assumption The single laboratory calibration transfer function, measured in a 55-gallon drum with anthracite smoke, applies to all 28 field sensors over 8 months of outdoor winter conditions.
Invoked in Section 2.5.2, calibration setup, and used to correct all Mongolian field data; no per-sensor field calibration is reported.
domain assumption Unmodified coal-burning gers serve as a valid control group for the modified gers, so differences in PM2.5 are attributable to insulation and fuel type.
Section 2.2 defines the control group and Section 3.1 interprets group differences; no randomization, baseline measurements, or behavioral covariates are provided.
domain assumption A single outdoor PM2.5 record from the U.S. Embassy monitor represents outdoor concentrations at all dispersed ger sites.
Section 3.1 uses the Embassy value to argue outdoor air is dirtier than indoor air; the sites are kilometers apart, as shown in Figure 1.
domain assumption A sensor placed on a center support beam of each ger captures occupant-relevant PM2.5 exposure.
Section 2.2 states indoor sensors were placed on a center support beam; no mixing or personal exposure validation is provided.
domain assumption The Sensirion SPS30's internal assumption of particle refractive index and density is valid for Mongolian coal smoke.
Section 2.1 notes the sensor calculates mass using refractive index and density; the authors do not verify these properties for the local aerosol.

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Cite this review

Pith. "Pith review of Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers." pith.science (2026). https://pith.science/paper/TRDQ7RRF

@misc{pith2026241117627,
  author       = {Pith},
  title        = {Pith review of: Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TRDQ7RRF}},
  note         = {Machine review of arXiv:2411.17627}
}

abstract

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbaatar, Mongolia, to measure $\mathrm{PM_{2.5}}$ concentrations inside yurts or "gers." Some of these gers were part of a non-government agency (NGO) initiative to improve the insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in $\mathrm{PM_{2.5}}$ concentrations, but these concentrations remained higher than levels recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in $\mathrm{PM_{2.5}}$ concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in $\mathrm{PM_{2.5}}$, though concentrations were still 6.4 times higher than those recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns in $\mathrm{PM_{2.5}}$ concentrations with slight variations. It was found that, at nighttime, the outside $\mathrm{PM_{2.5}}$ concentrations were generally higher than the inside concentrations of the gers in this study. This suggests that $\mathrm{PM_{2.5}}$ would flow into the gers whenever the doors were opened, causing spikes in $\mathrm{PM_{2.5}}$ concentrations.

Figures

Figures reproduced from arXiv: 2411.17627 by the authors.

**Figure 1.** Map of Ulaanbaatar, Mongolia showing geographical locations of LCSs in gers and in the U.S. Embassy, which can be seen on the far right in blue. The coal-burning modified ger is in red, the modified electric-only gers in orange, the unmodified gers in blue, and the modified hybrid gers in green. 2.3. Monitoring of the LCSs There was a two-pronged approach to LCS maintenance: cloud-based and in-person maintenance. Th… view at source ↗

**Figure 2.** Schematic of experimental setup for calibration. Data from the LCS in the barrel were plotted against the data from the TSI OPS for the same period. This yielded a second-order polynomial transfer function seen in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** (A) LCS data plotted against the TSI OPS data from the transfer function run. Trend line to data is plotted as a solid black line. The 95% confidence intervals to the fit is plotted as dashed lines. (B) Residual plot of fit to data [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Example of the diurnal pattern in PM2.5 concentrations showing cooking spikes for breakfast, lunch, and dinner. 3. Results and Discussion 3.1. Averaged Data [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Average PM2.5 values. 3.2. Daily and Nightly Averaged Data [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Averaged daytime PM2.5 values [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Averaged nighttime PM2.5 values [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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