ULAB Plume — Methodology Guideline

Version 1.11 · May 2026 · Pure Earth · chrisk@pureearth.org

1. Purpose and scope

ULAB Plume is a web-based screening tool for estimating the air, soil, and blood lead level (BLL) impacts of used lead-acid battery (ULAB) recycling operations on surrounding populations. It is designed for use in four contexts:

• Regulatory compliance screening: providing screening-grade impact estimates to support dialogue with environmental ministries
• Retrospective health-burden estimation: characterising the magnitude and footprint of past or ongoing exposure for impact assessment
• Advocacy and public-health communication: quantifying the spatial reach of contamination from a specific facility for public health communication
• Education: an interactive tool for learning how dispersion parameters influence pollution distribution, exposure pathways, and health burden

It is not a regulatory submission tool. Outputs are screening estimates with documented uncertainty bounds. For permit decisions or litigation, site-specific measurements and refined modelling are required. See more on use cases here.

1.5 About AERMOD and the LMIC application gap

What AERMOD is

AERMOD is a steady-state Gaussian plume dispersion model developed by the US Environmental Protection Agency (EPA) and the American Meteorological Society. It simulates how pollutants emitted from industrial sources disperse through the atmosphere, accounting for wind, atmospheric stability, terrain, building wake effects, and boundary-layer dynamics.

The model has been continuously developed since the late 1990s, reviewed extensively in peer-reviewed literature, and refined through real-world application across thousands of regulatory cases.

→ Official AERMOD page: epa.gov/scram/air-quality-dispersion-modeling-preferred-and-recommended-models

Regulatory standing of AERMOD (and what that does and does not mean for this tool)

AERMOD is the US EPA's preferred regulatory dispersion model for near-source air quality assessment (US EPA, 40 CFR Part 51, Appendix W — Guideline on Air Quality Models). This means it is the default tool used in the United States for:

• Permit applications: new or modified industrial facilities must demonstrate compliance with National Ambient Air Quality Standards (NAAQS) using AERMOD modelling
• Risk assessment: determining whether a facility's emissions threaten public health or welfare
• Litigation and enforcement: AERMOD outputs are admissible evidence in environmental enforcement cases
• Policy analysis: Environmental Impact Statements and air quality management plans rely on AERMOD modelling

Equivalent regulatory acceptance exists in the EU (under the EEA Industrial Emissions Directive), Australia, Canada, and many other high-income jurisdictions. AERMOD is not a screening tool — it is the authoritative regulatory model for industrial emissions in data-rich settings.

Important distinction: AERMOD's regulatory acceptance applies only to AERMOD when it is used with the data and procedures that regulators specify — including site-specific hourly meteorology, measured stack parameters, and verified emission factors. ULAB Plume uses AERMOD as one component of a wider screening pipeline that substitutes assumed values for several of these regulatory inputs. The outputs of ULAB Plume should not be treated as carrying the same regulatory weight as a conventional AERMOD modelling exercise. ULAB Plume is not currently validated for regulatory screening in LMIC settings; it is a research and advocacy tool intended to support public health communication and inform further investigation.

Why AERMOD is rarely used for LMIC smelter assessment

Despite its rigour, AERMOD is rarely applied to lead-acid battery recycling (ULAB) facilities in low- and middle-income countries. Three data gaps prevent direct use:

1. Meteorological data

AERMOD requires hourly meteorological observations covering at least one full year, typically including:

• Wind speed and direction
• Temperature and humidity
• Cloud cover and ceiling height
• Solar radiation
• Mixing height and atmospheric stability indicators

In high-income countries, these data are produced continuously by National Weather Service stations and meteorological agencies, freely available, and pre-processed into AERMOD-ready files (SFC and PFL format) using the AERMET preprocessor.

In LMIC contexts, the situation is fundamentally different:

• Meteorological station coverage is sparse, with many countries having fewer than a dozen stations nationwide
• Hourly data, where collected, is often paywalled, intermittent, or not in standard formats
• Upper-air radiosonde data needed for boundary-layer characterisation is collected at very few stations
• The AERMET preprocessing infrastructure is not routinely operated outside high-income jurisdictions

The result: in most LMIC settings, the meteorological inputs AERMOD expects simply do not exist, or exist in forms that require substantial reprocessing before AERMOD can use them.

2. Source emission data for LMIC ULAB recycling

Lead emissions from ULAB recycling in LMIC contexts come from a spectrum of facility types. In sub-Saharan Africa, recent fieldwork (Manhart & Adjei 2026) finds that the recycling itself is dominated by formal industrial plants — registered, licensed, and subject to regulatory oversight — operating at scales of 10,000–20,000 tonnes/year. Many of these formal plants operate with substandard practices: degraded baghouse maintenance, missing fume hoods, manual battery breaking, and uncontrolled slag handling. In other regions including South and Southeast Asia, smaller informal operations are also common. Both contexts present significant data gaps.

• Formal plants in many LMIC settings operate with substandard controls; published emission factors capture only nominal control performance, not real-world degraded operation
• Stack testing data is rare even for permitted formal facilities
• Informal/artisanal operations operate without permits and emit without oversight
• Particle size distributions of emitted lead have rarely been measured at LMIC ULAB sites
• Fugitive emissions (uncaptured releases from yard activities, raw material handling, slag disposal) are typically larger than stack emissions in substandard plants but almost never measured
• Operating schedules are often irregular and undocumented

The data needed to parameterise AERMOD for an LMIC ULAB site — emission rate per tonne processed, particle size distribution, stack geometry, temperature, exit velocity — is rarely available without direct site investigation.

3. Receptor and population data

AERMOD outputs concentration fields. Translating these into health risk estimates requires knowing where people live, work, grow food, and spend time. In high-income countries, fine-resolution gridded population data, land-use maps, and residential proximity records are routinely available. In LMIC settings, these are often sparse or outdated, particularly in informal settlements that frequently surround ULAB operations.

For a comprehensive reference of legally binding air quality standards for lead across LMIC contexts in Africa, Asia, and South America, see the Lead Air Legislation in LMICs page.

How this tool addresses the data gaps

ULAB Plume uses transparent, literature-based assumptions to fill each gap, while clearly documenting where assumed values are used and what their uncertainty bounds are.

Each assumption is documented in the relevant methodology section. The tool's outputs are presented with explicit caveats: as screening estimates suitable for advocacy and engagement, not as regulatory submissions or definitive risk assessments.

What this tool's outputs mean — confidence by pathway

The tool generates four kinds of output, and the confidence in each is different. Users should treat them with correspondingly different levels of caution.

Air concentration (highest confidence)

Predicted ambient air lead concentrations come directly from AERMOD's dispersion mathematics, applied to the user's emission inputs and the synthetic meteorology. AERMOD's dispersion physics is well-validated in temperate, mid-latitude settings. The main sources of uncertainty in LMIC contexts are: synthetic vs real meteorology, accuracy of emission rate inputs, and the absence of site-specific terrain. Predictions are best understood as order-of-magnitude correct for typical ULAB facility conditions.

Soil deposition (moderately high confidence)

Predicted deposition rates rely on the same dispersion mathematics plus AERMOD's particle-size-resolved dry deposition algorithm. Wet deposition is not modelled, which is a real limitation in monsoonal or tropical settings. Confidence is somewhat lower than for air concentrations — particularly for fine particles that travel further and where dry deposition velocities have wider uncertainty bounds.

Soil lead increment and BLL (moderate confidence)

Soil-Pb is derived from a linear-accumulation mass balance (deposition × exposure duration / depth × density) — conservative for typical soils on annual-to-decadal timescales, neglecting leaching, erosion, and root uptake. BLL increments are derived from direct US EPA IEUBK biokinetic-model outputs for the inhalation and soil/dust ingestion pathways, interpolated piecewise in log-log space between anchor concentrations. The IEUBK pathway anchors and the soil-mass-balance equation are documented in section 5. ΔIQ is derived from each receptor's modelled ΔBLL via the Crump et al. (2013) concurrent-BPb log dose-response. Both the soil accumulation model and the biokinetic dose-response have validity ceilings. Beyond them the tool suppresses the numeric soil-Pb and ΔBLL predictions rather than display extrapolated values; ΔIQ, by contrast, is capped at the value the dose-response gives at the validity ceiling (ΔBLL = 30 µg/dL) and labelled '≥' — a model floor rather than a hole — see section 5.4 for the validity treatment and threshold rationale.

Crop pathway and crop-attributable BLL (low confidence, screening only)

The crop layers and the crop contribution to ΔBLL apply a foliar-only deposition-uptake screening model with a composite transfer fraction R (Brown 2008 UK FSA / EC JRC TGD 2003 / US EPA OSWER 2002) that bundles interception, weathering, washoff, and (for cereals) threshing into a single literature-calibrated coefficient. Cereal R is empirically tuned against paired-measurement smelter sites in China (Liu 2020 Jiyuan; Liu 2022 Jiaozuo) — model predictions are within a factor of three of observed grain Pb at those sites. Root uptake is not modelled (small at the soil-Pb regime where direct deposition dominates near-field). The crop-to-BLL slope is a single linear coefficient (EFSA 2010 CONTAM Panel) applied to total dietary Pb intake; this is the largest open methodological gap at very high near-field intakes. The crop contribution is opt-in on the BLL surface (via the "Include crops" checkbox on the BLL panel) and is reported as a separate pathway in the response — users can read the crop heat maps (µg Pb / kg fresh-weight edible crop) without folding the crop pathway into ΔBLL or ΔIQ. The pathway is calibrated at screening precision and is only loosely validated against measured crop Pb at LMIC ULAB sites; see section 5.5 for caveats.

2. Atmospheric dispersion

2.1 Model

The tool runs AERMOD v24142, the US EPA's preferred regulatory dispersion model for near-source air quality assessment (US EPA Appendix W). AERMOD is a steady-state Gaussian plume model that includes:

• Pasquill-Gifford-Turner stability classification
• Briggs plume rise for buoyant and momentum-driven plumes
• Dry deposition with particle-size-resolved deposition velocities
• Boundary-layer-aware vertical mixing

AERMOD is configured with AVERTIME 1 PERIOD so that two averaging periods are captured per scenario run:

• PERIOD output — the full-year (8,760-hour) average concentration and total deposition at each receptor. These are the values used for the concentration / deposition / BLL / IQ heat maps; they are the appropriate quantity for an annual-impact screening assessment. PERIOD deposition is treated as annual with a unit conversion only (g/m² → mg/m²/yr); no time-extrapolation factor (annualisation_factor = 1.0).
• 1-HR worst output — the single highest 1-hour concentration at each receptor across the 8,760-hour simulation. This is captured separately on the response as concentration_1hr_worst_ug_m3 for NAAQS-style acute-exposure framing. It is not used on the chronic concentration / BLL / IQ heat maps; map values labelled "concentration" or "annual" are PERIOD-derived unless explicitly labelled "1-HR worst".

The dual capture reflects the tool's design intent: the heat maps screen chronic facility-attributable impact on the surrounding population; the 1-HR worst is available for users who need a separate acute-exposure framing alongside the chronic assessment. ULAB Plume is calibrated for chronic-exposure screening and is not intended as a 1-hour NAAQS-compliance modelling tool — the 1-HR worst is a transparency field, not a regulatory output.

The air concentration heat map includes a dashed reference contour at 0.5 µg/m³ corresponding to the World Health Organization's annual mean Air Quality Guideline for lead (WHO 2021). Receptors above this contour exceed the WHO recommended ambient air lead concentration.

2.2 Meteorological inputs

Real LMIC hourly meteorology is rarely available. Where it is, it is typically reanalysis-derived (MERRA-2, ERA5) and not in AERMOD's required SFC/PFL format. To bridge this gap, the tool generates synthetic AERMOD-format meteorology from STAR-format wind rose climatology for each supported region.

The pipeline:

1. STAR JSON input — Joint frequency table of (wind direction, wind speed bin, Pasquill stability class) for the representative city. 16 directions × 6 speed bins × 6 stability classes = 576 bins, with one frequency value per bin summing to 1.0.
2. Largest-remainder hour allocation — 8,760 annual hours are distributed across bins in proportion to their frequencies, using the largest-remainder algorithm to ensure exact total.
3. Diurnal placement — Each bin is assigned to specific hours of the year respecting class-specific time-of-day constraints:
   • Class A (very unstable): only 12–14h local time
   • Class F (very stable): only nighttime
   • Class D (neutral): any time
   The most-restricted classes are placed first to avoid scheduling conflicts.
4. ±11.25° direction jitter — Within each 22.5° direction wedge, individual hours are jittered uniformly (deterministic hash-based) to produce smooth heat map contours rather than 16-spoke artefacts.
5. Per-hour micrometeorology back-derivation — For each synthetic hour, AERMET-required parameters (friction velocity u*, sensible heat flux H₀, mixing height z_i, Monin-Obukhov length L) are derived from (wind speed, stability class) using AERSCREEN Appendix C lookup tables and Pasquill-Gifford-Turner relationships.
6. AERMET column-format output — SFC and PFL files are written to AERMET specification (CRLF line endings, fixed-width columns) and passed to AERMOD's SURFFILE/PROFFILE input lines.

2.3 Regional archetypes

The archetype library covers the following representative cities:

The four shipped archetypes deliberately span distinct ULAB-relevant climatological regimes so users can screen sensitivity to met:

• Abuja — inland West African savanna with a strongly bimodal regime (South-Westerly West African Monsoon Apr–Oct vs North-Easterly Harmattan Nov–Mar). Calmer near-surface flow than the coast; longer near-source residence times.
• Accra — coastal-tropical Gulf of Guinea. Persistent South-Westerly marine inflow dominates year-round (36.6%); Harmattan reaches the coast but is largely diluted, giving a less bimodal regime than Abuja. Mostly neutral / slightly unstable stability typical of warm maritime conditions.
• Dhaka — Ganges-Brahmaputra delta. Strongly bimodal Asian Monsoon (SW Jun–Sep / NE Nov–Feb) averages to a softer S peak (18%); lower wind speeds than coastal sites; strongly neutral stability (D 65%) with a full diurnal range (A 4%, F 0.5%) reflecting clear-sky inland nights.
• Delhi — continental Indo-Gangetic Plain. NW post-monsoon / Western Disturbance flow dominates the annual mean (19.8%). The strongest diurnal range of the LMIC set (A 7.1%, E 3.8%) — clear-sky days and nights inland away from maritime damping. Mean wind speed 3.0 m/s.

Each shipped archetype is derived from NASA MERRA-2 hourly reanalysis, sampled at the single grid cell containing the city's geographic centre (MERRA-2 native resolution: 0.5° lat × 0.625° lon, ≈50–70 km). For each calendar year, hourly 10 m wind components, surface sensible heat flux, and 2 m temperature are downloaded via the NASA GES DISC OPeNDAP service and binned into a STAR-format joint frequency table:

• 16 wind direction wedges (22.5° each, centred N, NNE, NE, …, NNW).
• 6 wind speed bins per AERMET conventions: <1.54, 1.54–3.09, 3.09–5.14, 5.14–8.23, 8.23–10.80, and ≥10.80 m/s.
• 6 Pasquill-Gifford-Turner stability classes (A–F). Each hour is classified from surface sensible heat flux and 10 m wind speed via the Pasquill-Gifford-Turner lookup (Pasquill 1961; Turner 1969 Workbook Table 2.1; EPA AERMET 2018). Calm hours (WS < 0.5 m/s) are routed to a separate calm-fraction field rather than placed in any A–F bin.

The resulting STAR climatology is fed to wind_rose_processor.py, which generates a synthetic but kinematically consistent 8,760-hour AERMET SFC + PFL pair for AERMOD to consume. Synthesis is deterministic (same JSON → same SFC) so a scenario re-run produces bit-identical met. Surface land-use parameters (roughness length, Bowen ratio, albedo) are sourced from regional literature defaults rather than MERRA-2 — documented per-city in the pipeline source.

Reproducibility: each archetype's source year and grid cell are recorded in the provenance block of its STAR JSON. The full ingest pipeline lives in backend/tools/merra2_to_star/ with unit tests at backend/tests/test_merra2_to_star.py; raw MERRA-2 fetches are gitignored as regenerable intermediates.

2.4 Receptor grid

Two receptor grid densities are available:

• Preview mode: 4 distance rings (50, 500, 5000, 50000 m) × 16 directions = 64 receptors. Designed for rapid scenario iteration.
• Final mode: 10 distance rings (50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000 m) × 36 directions = 360 receptors. Designed for publication-quality output.

Both modes anchor at 0°/N. The bilinear interpolation in the frontend heat map renders smoothly at either density.

The preview grid's coarse near-source spacing (50 m → 500 m → 5,000 m) under-resolves deposition gradients for coarse PSD bins (≳25 µm), whose deposition flux is concentrated within the first few hundred metres of the source and drops by several orders of magnitude before reaching the next ring. The polar-grid annular integration treats each ring's sampled flux as constant across its annular area, which is a poor approximation when the true flux varies steeply within the annulus. Final mode mitigates this with intermediate rings at 100, 200, 1,000, and 2,000 m. For analytical questions specifically about coarse-bin deposition behaviour (e.g., per-bin airborne-fraction-vs-distance), the tool also exposes a dense per-bin endpoint /api/per_bin_deposition that runs one AERMOD deck per PSD bin and returns the receptor field at the active mode's grid — the Results graphs tab uses this endpoint to render Graph 2 (airborne fraction per bin).

2.5 Validation status

End-to-end pipeline determinism was validated on 2026-05-06 by capturing the AERMOD inputs (synthetic SFC/PFL + input deck) for four scenarios via the /api/debug_dump endpoint, running AERMOD outside the deployed pipeline on those exact inputs, and comparing the locally-parsed receptor values against the deployed API's /api/run response. The four scenarios spanned both execution modes (preview 64 receptors, final 360 receptors), four emission profiles, throughputs from 1,000 to 23,500 tonnes/year, and both the pre-recalibration default set (three scenarios under v1.0 — LMIC bad / good / avg) and the post-recalibration set (one scenario under v1.1 — substandard at 15,000 t/y). Across 4,576 individual receptor-field comparisons (concentration and deposition, op-hours and annual averaged), every value matched to the float precision reported by AERMOD's output file. AERMOD v24142.

The pending validations are needed before this tool's outputs should be cited as authoritative. Until then, outputs should be presented as "screening estimates" with explicit uncertainty bounds.

3. Source emissions

3.1 Throughput-to-emission conversion

Annual lead emissions (Q, g/s) are derived from facility throughput using:

Q = (T × E × (1 - η)) / (3600 × H)

Where:

• T = annual throughput (tonnes lead recovered/yr)
• E = uncontrolled emission factor (kg Pb / tonne throughput)
• η = control device removal efficiency
• H = annual operating hours
• 3600 = seconds/hour

The default uncontrolled emission factor used by the tool is 12 kg Pb per tonne of ULAB feed, applied before abatement. The tool partitions this into a particulate share (90%) and a fume share (10%) and applies the selected control's particulate and fume efficiencies separately.

The 12 kg/t value is calibrated against the lead recovery data published in Manhart & Adjei (2026), which finds 95% recovery in low-standard formal plants and 99% in high-standard plants. The 5% loss in low-standard plants includes atmospheric emissions, slag, and other loss pathways; this tool's emission factor reflects the atmospheric share (approximately 30–40% of total loss). Direct stack emission measurements at LMIC plants would refine this estimate; this is flagged as a pending validation.

3.2 Emission controls

Five control / facility-type presets, recalibrated for African ULAB context per Manhart & Adjei (2026):

3.3 Particle size distribution (PSD)

Stack emissions are distributed across six particle size classes based on the selected PSD preset. Two consolidated presets cover the spectrum of off-gas treatment:

The PSD affects deposition velocity (larger particles deposit closer; smaller particles travel further) and is a critical determinant of the spatial pattern of soil and food impacts. Both presets are informed by qualitative findings in Manhart & Adjei (2026); direct field PSD measurements at LMIC plants are not currently available, so these distributions are best estimates pending field validation.

Note on fugitive emissions: Manhart & Adjei (2026) document that fugitive emissions from furnace charging, refining kettles, manual battery breaking, and slag handling can constitute a substantial fraction of total facility emissions in substandard plants. As of v1.1 (May 2026) the tool models these fugitives as a co-located ground-level area source — see section 3.5.

3.4 Operating duty cycle

Real ULAB facilities operate intermittently (typical: 8 hours/day × 250 days/year = 2,000 hours/year). The tool accepts user-specified operating hours and applies a duty-cycle factor:

duty_cycle = (op_hours_per_day × op_days_per_year) / 8760

Two concentration fields are surfaced per receptor: the period-mean concentration during operating hours (no duty-cycle scaling — the concentration a child breathes when the facility is emitting) and the annual time-average across the full year (period-mean × duty cycle — the concentration averaged over all 8,760 hours including non-operating periods). The annual time-average drives the BLL pathway because IEUBK is calibrated for chronic ambient exposure; the during-operating-hours mean is exposed for users assessing what the air is like when the plant is running. Deposition fields are scaled by duty cycle in the same way. The 1-HR worst concentration field (section 2.1) is independent of duty cycle — it is the single worst modelled hour, irrespective of whether that hour falls inside or outside the operating window.

3.5 Fugitive emissions

Fugitive emissions — uncaptured releases from yard activities, raw-material handling, manual battery breaking, refining kettles, and slag handling — can dominate facility-total Pb releases at substandard LMIC plants (Manhart & Adjei 2026; Ericson et al.). Direct field measurements distinguishing per-process fugitive sources are unavailable for LMIC ULAB plants, so the tool implements a screening-grade lumped-fraction model rather than a per-process inventory.

A user-editable fugitive fraction ffug splits the total emission inventory into stack and fugitive components:

E_stack = (1 − f_fug) × E_total
E_fug   = f_fug × E_total

The total emission inventory Etotal is computed from throughput and the uncontrolled emission factor exactly as in section 3.1; ffug only changes how that inventory is partitioned between sources. Default values per scenario:

Fugitives are modelled in AERMOD as a square AREA source centred on the stack location. Default geometry: 100 m side length, 2.5 m release height, 1.5 m initial vertical dimension (σ_z0), all user-editable. The fugitive PSD is coarser and lower-density than the stack PSD, reflecting mechanically generated mineral-matrix dust rather than condensation aerosol:

The mineral-matrix densities (3–4 g/cm³) sit well below the stack-PSD speciation schedule (7–11 g/cm³ for fume condensate and Pb compounds) because fugitive dust is dominated by soil and slag matrix with embedded Pb inclusions, not pure Pb compounds. Density feeds AERMOD's METHOD_1 dry-deposition velocity calculation directly; using stack-mirror densities would overstate near-field deposition by roughly the density ratio for the coarse bins.

Implementation note (preview vs final mode). AREA-source AERMOD is approximately 14× slower than POINT-source at the same receptor count because AERMOD performs adaptive Romberg integration over the source area for every receptor × meteorology hour combination. To keep preview mode interactive, the fugitive AERMOD run uses a coarse 8-direction polar receptor grid (32 receptors) and bilinearly interpolates the dispersion fields onto the stack-mode receptor grid; the source-contribution readouts at 50 m and 500 m are read from the AERMOD-native coarse receptors at the closest coarse direction, so no interpolated values appear in the sanity readout. Final mode uses the full receptor grid for both stack and fugitive — no interpolation — at the cost of substantially longer wallclock (~20–40 minutes for the fugitive subprocess alone).

Status of fugitive defaults. The fugitive-fraction defaults (0.10 / 0.35 / 0.60) and PSD parameters (diameters, mass fractions, densities) are screening-grade choices informed by qualitative literature, not literature-anchored against site-specific measurements. A formal sensitivity check across these parameters is required before tool outputs with fugitives enabled are used in advocacy or regulatory contexts. As with stack emissions, the most reliable path to higher-confidence fugitive outputs is field measurement at representative LMIC sites — see Pure Earth's research agenda.

Note on 1-HR worst summing. When both the stack and fugitive AERMOD runs report a 1-HR worst-hour concentration at the same receptor, the combined receptor's 1-HR worst is the arithmetic sum of the two sources' values. This is an upper-bound approximation: the worst hour for the stack source may not coincide temporally with the worst hour for the fugitive source, so the true joint maximum could be lower. Resolving the joint maximum exactly would require hour-by-hour data per source; the conservative sum is accepted as a screening-grade approximation, bounded above by 2× for two independent sources. PERIOD-averaged concentration / deposition / BLL / IQ values are not affected — they sum exactly because the averaging window covers all hours of both sources.

4. Soil accumulation

4.1 Mass-balance model

The tool estimates the increment in topsoil lead concentration caused by atmospheric deposition over a user-specified operational duration, assuming:

• All deposited lead remains in the surface soil layer of specified depth (no leaching, no erosion)
• The soil layer mixes uniformly within itself (no vertical gradient)
• Background soil lead is independent and additive

ΔPb_soil (mg/kg) = (D × T) / (d × ρ × 10)

Where:

• D = annual deposition rate (mg/m²/yr)
• T = operating years
• d = soil depth (cm)
• ρ = bulk density (g/cm³, default 1.3 — typical mineral surface soil at the Soil tab's exposure framing)

The Soil tab and the BLL pathway both compute soil-Pb via this mass balance over the same operational-years duration (default 20 yr, set by the "Smelter operational years" control on the Soil tab), but apply different mixing depth and bulk density appropriate to their framings: the Soil tab targets agricultural / root-zone questions (15 cm depth, ρ = 1.3 g/cm³); the BLL pathway targets the IEUBK child-residential contact layer (1 cm depth, ρ = 1.5 g/cm³ — see section 5.3). Same accumulating deposition and duration; the depth/density differ because they answer different questions about the same soil column.

Validity cap. Predicted soil-Pb is clamped at 80,000 mg/kg on both pathways. This is the upper bound of soil-Pb observed at the most severe documented secondary-smelter sites (e.g. Doe Run Herculaneum surface soils; ASARCO El Paso). The linear-accumulation model produces non-physical values above this — at very high deposition rates, deposited Pb begins to accumulate in distinct surface phases rather than mixing uniformly within the bulk-density-defined soil column, so the model's uniform-mixing assumption fails. Receptors past the cap are flagged for off-scale display rather than presenting a numeric value (see section 5.4).

4.2 EPA bin thresholds

The soil heat map is anchored to US EPA action levels:

4.3 Limitations

The "no leaching, no erosion" assumption is conservative for typical soils on decadal timescales but overestimates accumulation on:

• Sandy, high-permeability soils with significant rainfall infiltration
• Steeply sloped or actively eroding sites
• Tilled agricultural soils (homogenisation extends mixing depth)

Users should select soil depth appropriate to the exposure pathway:

• Direct ingestion (children playing in dust): top 1 cm
• Vegetable garden uptake: top 15 cm (root zone)
• Cereal/staple crops: top 30 cm (tilled depth)

The linear-accumulation assumption also breaks down at the upper end of the deposition range, where the soil column cannot homogeneously incorporate the deposited mass. The 80,000 mg/kg cap (section 4.1) is the model's explicit validity boundary; receptors past it carry no numeric soil-Pb prediction. The cap is anchored to the highest documented secondary-smelter surface soils, not to a theoretical absorption limit. Above the cap, the receptor's spatial location is still shown on the map (with an off-scale visual marker) so users see where severe contamination occurs even when the absolute concentration cannot be honestly predicted.

5. Blood lead level (BLL) impact estimation

5.1 Conceptual framework

This tool estimates the increment in child blood lead level (ΔBLL) attributable to a specific facility's emissions, decomposed into three pathways:

• Inhalation — direct uptake of airborne lead. Driven by the receptor's annual mean ambient PbA concentration. Anchored to direct IEUBK biokinetic-model outputs (section 5.2).
• Soil / dust — incidental ingestion of accumulated atmospheric deposition. Driven by the cumulative deposition over the smelter's operational years (default 20 — the "Smelter operational years" control on the Soil tab, shared with the soil-accumulation model), apportioned across the IEUBK contact depth (1 cm). Linear in this duration. Anchored to direct IEUBK biokinetic-model outputs (section 5.3).
• Crop (foliar deposition) — ingestion of edible crops grown on receptor land, with deposited Pb retained on foliage and partially translocated to the edible portion. Screening-grade implementation of the food-crop foliar-uptake form with a composite transfer fraction R (Brown 2008 UK FSA / EC JRC TGD 2003 / US EPA OSWER 2002 for leafy; + Liu 2020/2022 paired-measurement empirical tuning for cereal). Converted to ΔBLL via a single linear child-dietary slope (EFSA 2010 CONTAM Panel). Off by default on the ΔBLL surface — see section 5.5.

The inhalation and soil pathways use direct outputs from the EPA IEUBK biokinetic model, anchored at concentration points (8 for inhalation, 6 for soil/dust) and interpolated piecewise in log-log space between anchors. The crop pathway is computed independently and added to the running total in linear ΔBLL space. The response carries two totals — delta_bll_total_excluding_foliar_ug_dl (air + soil) and delta_bll_total_including_foliar_ug_dl (air + soil + crop) — and the corresponding validity flags for each; the BLL surface and the ΔIQ gating switch between them based on the user's "Include crops" checkbox on the BLL panel (section 5.4). Other potential pathways (water, paint, occupational, dietary contamination from non-aerial sources) are not modelled — this tool isolates the contribution attributable to the modelled facility, not the population's total BLL. Implementation lives in backend/app/bll.py + backend/app/crops.py, with JS mirrors at frontend/bll.js + frontend/app.js.

5.2 Inhalation pathway

The IEUBK model was run directly across the relevant outdoor PbA range, assuming indoor air = 30% of outdoor air (US EPA convention for residential exposure). Eight IEUBK anchor points span the range from 0.15 to 150 µg/m³, with denser sampling at the lower end where most modelled receptor concentrations fall:

For an arbitrary PbA between two anchors, ΔBLL is computed by piecewise log-log linear interpolation:

log(ΔBLL) = log(BLL_lo) + t × (log(BLL_hi) − log(BLL_lo))
where t = (log(PbA) − log(PbA_lo)) / (log(PbA_hi) − log(PbA_lo))

Below 0.15 µg/m³ outdoor air, IEUBK predictions are below the resolution at which the model produces meaningful output. The tool linearly extrapolates toward zero in this regime (slope ≈ 0.67 µg/dL per µg/m³); values below 0.1 µg/dL ΔBLL fall outside the heat map's display range. At and above 150 µg/m³ the function returns the saturation cap of 30 µg/dL and raises an air-saturation flag — see Section 5.4.

5.3 Soil pathway

Atmospheric deposition is converted to a topsoil Pb concentration via a simple mass balance:

Pb_soil (mg/kg) = D × t / (ρ × z)

where D is annual deposition (mg/m²/yr), t is exposure duration (yr), ρ = 1500 kg/m³ is bulk soil density (typical mineral surface soil), and z is mixing depth (m). The resulting soil-Pb concentration is then mapped to ΔBLL via direct IEUBK output anchors:

IEUBK assumes default soil ingestion rate and lead bioavailability for 6–7 yr-old children in a residential setting. Interpolation is piecewise log-log linear, identical in form to the inhalation pathway. Below 50 mg/kg the function extrapolates linearly through the origin (slope 0.002 µg/dL per mg/kg). At and above 1600 mg/kg the function returns the saturation cap of 3.2 µg/dL and raises a soil-saturation flag.

Unified exposure duration. The exposure duration is shared across the soil-accumulation model and the BLL soil pathway: a single control — "Smelter operational years" (t, default 20 yr) on the Soil tab — drives both. The BLL soil pathway's mixing depth is fixed at z = 1 cm (the IEUBK contact-relevant residential contact depth — the surface dust layer for incidental hand-to-mouth ingestion) and is no longer separately user-tunable. The soil-Pb intermediate scales linearly with t up to the 80,000 mg/kg cap, but ΔBLL is not linear in t because the IEUBK soil curve is non-linear and saturates at its anchor table top (1,600 mg/kg → 3.2 µg/dL ΔBLL).

The same operational-years value feeds the Soil tab's own topsoil heat map, which applies a different depth and density (z = 15 cm, ρ = 1.3 g/cm³ — the agricultural root zone) to answer a different physical question about the same soil column. Same accumulating deposition over the smelter's operating life, same duration; the two views differ only in the soil layer each cares about — the child's surface contact layer vs. the agricultural root zone. Unifying t is correct because the smelter operates over years, and that accumulating Pb is seen by both layers over the same period.

Soil-Pb validity cap. The intermediate soil_pb_mg_kg is clamped at 80,000 mg/kg, matching the documented upper bound of surface-soil Pb at the most severely contaminated secondary-smelter sites (Doe Run Herculaneum; ASARCO El Paso). The cap reflects the linear-accumulation model's validity boundary, not a physical maximum. Receptors whose raw soil-Pb would exceed the cap are flagged (soil_pb_capped) for off-scale visual treatment — see section 5.4. The IEUBK soil-pathway BLL contribution from these receptors is unaffected by the cap (the IEUBK table itself saturates at 1,600 mg/kg, well below 80,000); only the reported soil-Pb number is suppressed.

The inhalation pathway is independent of t and z. Moving the Smelter operational years slider re-runs both the Soil-tab accumulation map and the BLL soil pathway (and the combined total + ΔIQ derived from it) in JS, with no AERMOD re-execution.

5.4 Combined ΔBLL and model validity treatment

The tool tracks two facility-attributable ΔBLL totals per receptor, so the user can switch the rendered BLL surface and the downstream ΔIQ gating between an air+soil-only framing and an air+soil+crops framing without re-running AERMOD:

ΔBLL_total_excluding_foliar = ΔBLL_air + ΔBLL_soil
ΔBLL_total_including_foliar = ΔBLL_air + ΔBLL_soil + ΔBLL_foliar

The "Include crops" checkbox on the BLL Impacts panel flips between the two. Both totals are computed on every receptor; the checkbox only changes which one is used for the surface, the summary, the validity gating, and the ΔIQ recompute.

Each receptor is classified into one of four states based on where its inputs and outputs fall relative to the underlying models' validity ranges. The state determines what the map and summary panel show. The validity flag follows the active total: when crops are off the BLL surface, the excluding-crop validity flag (bll_validity_exceeded_excluding_foliar) drives state assignment; when crops are on, the including-crop flag (bll_validity_exceeded_including_foliar) drives it. Both flags are present on every response so consumers can audit either framing.

States 2, 3, and 4 are independent: a single receptor can be in multiple states simultaneously (typically state 3 + state 4 near-source for high-throughput informal scenarios). On the map, the off-scale hatched-square marker takes visual priority over the ✕ marker when both would apply.

State 2 — input-table extrapolation. The IEUBK anchor tables for both pathways cover PbA up to 150 µg/m³ (air) and soil-Pb up to 1,600 mg/kg (soil). Above either anchor table top, the pathway's BLL contribution is held at the table's saturation value — physiologically motivated by gastrointestinal absorption saturation (the fraction of ingested Pb absorbed into circulation falls as ingestion load rises) and erythrocyte binding saturation (Pb bound to red blood cells stops increasing linearly with circulating Pb). The total ΔBLL is still a meaningful biokinetic estimate provided it sits below the output validity threshold; the ✕ marker signals "input extrapolated, output still valid".

State 3 — BLL past model validity (≥ 30 µg/dL). The biokinetic model underpinning ΔBLL prediction (IEUBK, which handles both the inhalation and soil/dust pathways) is validated for chronic-exposure BLLs up to approximately 25–30 µg/dL. Above 30 µg/dL the model's steady-state assumptions cease to hold: at these BLLs a child is in the clinical-evaluation regime rather than chronic-exposure dynamics, and the linear/log dose-response curves leave their validity range. ULAB Plume's 30 µg/dL threshold is the standard defensible upper bound for IEUBK output validity; the CDC clinical chelation threshold is 45 µg/dL, placing the validity boundary between chronic-exposure relevance and acute toxicity. For receptors in this regime:

• The numeric ΔBLL is suppressed on the map and in tooltips — displaying it would be false precision past the model's predictive range.
• The receptor is shown with a hatched off-scale marker so users still see where these receptors are located, even when the magnitude cannot be honestly predicted.
• ΔIQ is capped at the IEUBK validity ceiling — computed as if ΔBLL = 30 µg/dL, returned as a normal numeric value on the API response, and shown in tooltips with a '≥' prefix and a 'model validity exceeded' note. Rather than extrapolate Crump 2013 up an unbounded curve (over-stating) or suppress to null (which holed the ΔIQ surface over the worst-exposed receptors), the tool reports the minimum IQ loss consistent with reaching the ceiling.
• The BLL summary panel surfaces a categorical count: "N receptors in saturated zone (≥ 30 µg/dL) — biokinetic model validity exceeded. In the clinical acute-toxicity regime; CDC clinical chelation threshold is 45 µg/dL." The highest ΔBLL among receptors where the model is valid is reported as a separate subnote.

ΔIQ at the validity ceiling (cap, not suppression). Unlike the numeric ΔBLL — suppressed for State-3 receptors because the biokinetic model cannot predict its magnitude — ΔIQ for these receptors is capped: Crump 2013 is evaluated as if ΔBLL were exactly 30 µg/dL, giving a fixed floor of ≈7 IQ points lost at the default background BLL and central CI band; the exact value shifts with background BLL and band. The heat map renders these receptors at the ceiling value and the cursor shows '≥ X.X IQ pts — model validity exceeded'; the hatched off-scale marker still flags them. A child whose facility-attributable ΔBLL has crossed 30 µg/dL has at least the loss the model predicts at 30, so the ceiling is an honest lower bound — communicating 'at least this severe' without the holes that null-suppression punched over the highest-exposure receptors.

State 4 — Soil-Pb past model validity (≥ 80,000 mg/kg). Independently of the BLL output validity, the linear-accumulation soil model itself has a validity boundary at 80,000 mg/kg (see section 4.1). Receptors past this cap carry no numeric soil-Pb prediction but are still shown on the map with a hatched marker on the soil layer. Because the IEUBK soil table already saturates at 1,600 mg/kg (well below the 80,000 cap), the soil-pathway BLL contribution is unaffected by the cap — only the displayed soil-Pb number is suppressed.

The four-state classification is part of the methodology, not just a UI choice. It is the mechanism by which the tool avoids over-claiming: where the underlying physics, biokinetics, or dose-response models cannot honestly predict a magnitude, the tool refuses to fabricate one — suppressing the number (soil-Pb, ΔBLL) or capping it at the model's validity ceiling and labelling it '≥' (ΔIQ), always with an off-scale marker on the receptor. Users see the spatial extent of "off-scale" exposure without the tool fabricating precise values past its validity range.

5.5 Crop pathway (foliar deposition)

The crop pathway estimates the lead concentration in edible crops grown on receptor land, and the contribution of crop ingestion to facility-attributable ΔBLL. The v1.11 pathway is a screening-grade implementation of the food-crop foliar-uptake form with a single composite transfer fraction per crop class — calibrated against the food-crop Pb literature (Brown 2008 UK FSA; EC JRC TGD 2003; US EPA OSWER 2002) and, for cereals, empirically tuned against paired-measurement smelter sites in China (Liu 2020 Jiyuan; Liu 2022 Jiaozuo). It replaces both the v1.0 slope-factor food pathway (retired in v1.2 for poor LMIC validation) and the v1.10 6-parameter IAEA TRS-364 implementation that shipped briefly before this rewrite — the v1.10 form double-counted interception by carrying separate interception (R) and weathering/transfer (W, T) terms whose composite was already baked into the published food-crop literature values.

Scope: foliar only

Only the foliar (above-ground) deposition uptake pathway is modelled: atmospherically deposited Pb intercepted by leaf and stem surfaces, retained against weathering, washoff, and (for cereals) threshing/cleaning losses. Root uptake from contaminated soil — Pb already in the soil being taken up via the roots — is not included. The omission is deliberate: at relevant LMIC ULAB soil-Pb levels (10–10⁴ mg/kg), measured soil-to-plant transfer coefficients for Pb are very low (typically 10⁻³ to 10⁻² for leafy crops, 10⁻⁴ to 10⁻³ for cereals), and the direct foliar interception of fresh deposition dominates the predicted crop Pb at near-field receptors. Beyond ~3 km from the source the foliar contribution falls off and root uptake of accumulated historical soil-Pb may become significant — the tool's framing in that far-field regime is "screening for facility-attributable contamination from current-year deposition", not "total crop Pb measurement". A future iteration may add a root-uptake term once LMIC-specific soil-to-plant transfer factors are calibrated — see FUGITIVE_FOLLOWUPS.md #8.

Equations

For each receptor and each crop class, edible-tissue Pb concentration is:

C_foliar (mg/kg FW) = (D_ann × f_grow × R × (1 − W)) / Y_fw

where
  D_ann     annual atmospheric Pb deposition (mg/m²/yr, AERMOD output)
  f_grow    fraction of the year the crop is exposed = t_grow_days / 365
  R         composite transfer fraction (food-crop literature; bundles
             interception, field weathering, rain washoff, and for cereals
             threshing/cleaning into a single calibrated coefficient)
  W         additional consumer wash removal at the kitchen step (0 by
             default — LMIC-conservative). Applied AFTER R has handled
             field weathering, so W represents only the additional removal
             from vigorous washing of the as-harvested crop.
  Y_fw      edible fresh-weight yield per unit ground area, kg/m²

The equation deliberately drops the separate interception term I that appears in radioecological formulations (IAEA TRS-364, ECOSYS-87, FDMT). The food-crop Pb literature reports R as a composite that is already calibrated against field measurements of crop concentration per unit deposition flux — multiplying by an additional I term would double-count interception and over-predict crop Pb by a factor of 2–5 against measured grain Pb at validation sites.

Combined-class intake and ΔBLL:

Intake_leafy   = C_foliar_leafy   × G_leafy   / 1000        (µg Pb/day; G in g/day)
Intake_cereal  = C_foliar_cereal  × G_cereal  / 1000
ΔBLL_foliar    = (Intake_leafy + Intake_cereal) × β_diet
β_diet         = 0.105 µg/dL per µg Pb/day (EFSA 2010 CONTAM Panel, child)

The implicit deposition-to-edible-Pb constant k = (f_grow × R) / Y_fw at central defaults is:

• Leafy: k = (45/365) × 0.10 / 3.0 = 0.00411 mg/kg FW per mg/m²/yr
• Cereal: k = (120/365) × 0.005 / 0.6 = 0.00274 mg/kg FW per mg/m²/yr

Both k values are asserted in the module's self-check (app/foliar_uptake.py::_self_check) so any drift in the defaults fails the test suite loudly. At the TA Luft annual deposition limit (36.5 mg/m²/yr), combined-class ΔBLL_foliar is 2.36 µg/dL. The 1-µg/dL foliar-only ΔBLL crossover occurs at D = 15.5 mg/m²/yr.

Defaults

Five parameters per crop (t_grow_days, R, Y_fw, W, G) plus the shared dietary BLL slope β_diet are exposed for user override on the Crops tab via an "Override defaults" checkbox. Sliders are disabled by default; ticking the checkbox enables them and sends a foliar_params block on the next /api/run. β_diet default is 0.105 µg/dL per µg/day (EFSA 2010 CONTAM Panel child); slider range 0.02–0.20 covers the adult value (~0.018) through the upper end of older child-cohort literature. Override-at-defaults reproduces no-override results bit-for-bit.

Outputs

The pathway adds four per-receptor fields to the /api/run response — foliar_pb_leafy_ug_kg, foliar_pb_cereal_ug_kg, foliar_intake_leafy_ug_day, foliar_intake_cereal_ug_day — plus the single delta_bll_foliar_ug_dl increment that is folded into delta_bll_total_including_foliar_ug_dl. Three heat map layers expose the pathway:

• Exposure → Crops (leafy) and Crops (cereal) — mg Pb / kg fresh-weight edible crop, with a dashed reference contour at 0.01 mg/kg — "threshold for 1 µg/dL diet". The 0.01 mg/kg anchor is the food concentration at which a full daily diet (~1 kg/day total child food consumption) at this concentration would produce ΔBLL ≈ 1 µg/dL via the EFSA 2010 dietary slope. The EU 2023/915 maximum levels (0.30 mg/kg leafy, 0.20 mg/kg cereal grain) remain visible as palette break boundaries.
• BLL impacts → Crops — the combined leafy + cereal contribution to child ΔBLL (delta_bll_foliar_ug_dl) rendered on the same palette as the other ΔBLL sub-layers. Off-scale validity marker on this layer always uses the including-foliar validity flag (crops only contribute to that total), regardless of the "Include crops" checkbox state.

The "Include crops in ΔBLL surface" checkbox on the BLL panel adds the crop pathway to the combined BLL surface and the ΔIQ gating — when unticked, the combined BLL surface shows air+soil only. The crop sub-layer and per-class µg/kg layers are always available regardless of the checkbox.

Caveats

• Linear BLL slope. The crop-to-BLL slope is a single linear coefficient applied to total dietary Pb intake. At low-to-moderate intakes (a few tens of µg/day) the linear approximation is defensible — the EFSA derivation is itself anchored at this regime. At very high intakes (hundreds of µg/day, which the screening pathway can produce at near-field high-throughput substandard scenarios) the linear form materially over-predicts the actual BLL impact because gastrointestinal absorption saturates and erythrocyte binding becomes non-linear. Non-linear absorption is the largest open methodological gap in the crop pathway — see FUGITIVE_FOLLOWUPS.md #10. The BLL validity threshold from section 5.4 (≥ 30 µg/dL) is the operational guardrail: receptors where the linear extrapolation pushes ΔBLL past 30 µg/dL are flagged as off-scale, and ΔIQ is capped at the validity-ceiling value (§5.4) rather than extrapolated.
• Foliar-only, no root uptake. Root uptake is omitted (see "Scope" above). For receptors where atmospheric deposition is low but historical soil-Pb is high, the crop pathway will under-predict actual crop Pb. The tool's framing is "screening for facility-attributable crop contamination from current deposition", not "total crop Pb measurement".
• Annual deposition, not monthly. The growing-window deposition is approximated as D_annual × f_grow — i.e. the annual mean rate scaled by the fraction of the year the crop is in the field. This assumes the crop's growing season catches the same mean deposition rate as the annual average. Real LMIC contexts often have strongly seasonal deposition (monsoon vs dry season) and crops grown specifically in the dry season. A future iteration could substitute monthly-resolved deposition — see FUGITIVE_FOLLOWUPS.md #7.
• BLL slope is for children. The EFSA 2010 child dietary slope underlies the conversion. Adults absorb a smaller fraction of dietary Pb (~10% vs ~50% for children) so the same intake produces a smaller ΔBLL in adults. The map currently surfaces child ΔBLL only — see FUGITIVE_FOLLOWUPS.md #9.
• Two crop classes only. Real diets include roots and tubers (potato, cassava, yam), legumes, fruits, animal products, etc. The screening tool collapses this into "leafy" (high composite R, harvested whole) vs "cereal" (low composite R reflecting the small grain-translocation fraction). Users with site-specific diet data can map their crop mix onto these two classes by adjusting R and Y_fw on the Crops tab.
• Cereal R is empirically tuned against two paired-measurement sites only. The reduction of central cereal R from the literature consensus (0.01–0.02) to 0.005 is anchored to Liu 2020 Jiyuan TS and Liu 2022 Jiaozuo. These are heavily contaminated industrial sites in China; transportability to LMIC ULAB-specific contexts (where the dominant Pb species and surface deposition conditions may differ) has not been validated. Cereal predictions carry larger uncertainty than leafy predictions for that reason.

References

Key references for the crop pathway (full citations in Section 7):

• Brown, S.L. (2008). UK Food Standards Agency systematic review of food-crop Pb transfer fractions. Provides the leafy-vegetable composite-R consensus (0.05–0.15).
• European Commission Joint Research Centre (2003) — Technical Guidance Document on Risk Assessment. Source of the food-crop R framework for the EU industrial-emissions screening pipeline.
• US EPA OSWER (2002) — Multimedia, Multipathway, and Multireceptor Risk Assessment Protocol for Hazardous Waste Combustion Facilities. The third independent systematic review converging on the leafy R = 0.10 central value.
• Chamberlain, A.C. (1970) — Interception and retention of radioactive aerosols by vegetation. Atmos Environ 4(1): 57–78. Original empirical basis for the cereal R range of 0.01–0.05 on smooth grain surfaces.
• Liu Y. et al. (2020) — paired soil/crop measurements at Jiyuan TS lead smelter (China). D ≈ 350 mg/m²/yr; grain Pb 0.12–0.48 mg/kg. Primary cereal R tuning reference.
• Liu et al. (2022) — Jiaozuo smelter paired-measurement study. D ≈ 300 mg/m²/yr; grain Pb 0.62 mg/kg. Second cereal R validation point.
• EFSA Panel on Contaminants in the Food Chain (CONTAM) (2010) — Scientific opinion on lead in food. Source of the child dietary BLL slope (β_diet = 0.105 µg/dL per µg/day).
• Carrington, C.D. et al. (2019) — FDA Interim Reference Level (IRL) framework for child dietary Pb. Cross-reference for the BLL slope.
• European Commission Regulation (EU) 2023/915 — maximum levels for certain contaminants in food. Source of the reference contour values (200 µg/kg for cereal grain, 300 µg/kg for leafy vegetables).

5.6 IQ impacts from blood lead level

The ΔIQ map layer translates each receptor's modelled facility-attributable ΔBLL into a predicted IQ-point deficit using a published cohort dose-response. The IQ layer is built on top of the BLL pipeline — it does not require an additional AERMOD run, and switching background BLL or CI band on the BLL Impacts tab triggers an instant client-side recompute without a backend round-trip.

IQ dose-response specification

The tool uses a single, hard-coded dose-response: the Crump et al. (2013) log model on concurrent blood lead level, from Table 5 of the paper:

ΔIQ = β × ln(BPb + 1)
β   = −3.315 IQ points per ln(µg/dL + 1)
95% CI of β: (−4.546, −2.084)

This is the only dose-response the production UI exposes. There is no model selector — both because all six earlier candidate specifications (Lanphear-family and Crump linear variants) are dominated on epidemiological grounds, and because exposing the choice to non-specialist users invited misuse without improving the answer.

Why this specification?

Lanphear et al. (2005) is superseded. The seven-cohort pooled-analysis published by Lanphear and colleagues was the canonical low-dose Pb-IQ dose-response for nearly a decade, but Crump et al. (2013) is a refit of the same cohort data with corrected confounder treatment, a fixed boundary condition at BPb = 0, and full reporting of all five candidate BPb temporal measures under both functional forms. Crump 2013 should be preferred wherever the choice would otherwise have been Lanphear 2005; the underlying epidemiology is identical and the statistical treatment is improved.

Concurrent BPb is the right temporal measure for a screening tool. Crump 2013 reports five candidate BPb measures (concurrent, peak, early childhood, lifetime average, 24-month). For a steady-state screening application that maps a chronic facility-attributable ΔBLL onto IQ outcomes, "concurrent" — BPb measured at the same time as the IQ test — is the temporal measure most closely matched to the modelled exposure. Peak and lifetime-average measures answer different questions (worst-acute-exposure neurotoxicity vs. cumulative-burden trajectories) and are not the right framing for a permanent facility footprint.

The log form covers the full observed BPb range. Crump fits each of the five BPb measures under two functional forms: the log specification above and a linear specification (ΔIQ = β × BPb) that is adequate only below ~10 µg/dL. Near-source ULAB receptors routinely sit above that cutoff once facility ΔBLL is added on top of background; the log form is the only specification that stays valid across the full observed range and that satisfies the physically required ΔIQ = 0 at BPb = 0 boundary condition.

Alternative dose-responses considered but not used

The backend's compute_iq_loss() function retains the full set of nine Crump 2013 alternative specifications (4 alternative BPb temporal measures × 2 functional forms, plus the linear form on concurrent BPb), pinned to Crump Table 5 in the unit-test suite. They are kept on the public API surface — but not exposed in the UI — for three reasons:

• Regression armour. Pinning every β against the published table in unit tests means accidental edits to any coefficient fail loudly. Removing the specifications would remove that guardrail.
• Reproducibility. A researcher rerunning a historical scenario, or comparing this tool's output against a published Crump-table application, can pass model=... programmatically to reproduce a prior dose-response choice.
• Future sensitivity tooling. An advanced internal analysis mode (not user-facing) may later sweep the alternatives to bound dose-response uncertainty. The coefficients are zero-cost to keep available.

The Lanphear et al. (2005) coefficients themselves are not implemented. Anyone needing a Lanphear comparison should use Crump 2013 with the same cohorts and refer to the Lanphear paper directly for the unadjusted figures.

The 95% confidence interval band represents the statistical uncertainty in the cohort meta-analysis β coefficient — it does not bracket dispersion uncertainty, PSD uncertainty, IEUBK biokinetic uncertainty, individual susceptibility variation, or LMIC transportability. Total predictive uncertainty including those terms is substantially wider than the displayed CI band. The CI band sub-toggle on the BLL Impacts tab lets users render the lower (more loss) and upper (less loss) bounds separately for sensitivity assessment.

Why ln(BPb + 1) and not ln(BPb)

The +1 inside the logarithm is load-bearing. ln(BPb) diverges to −∞ as BPb → 0, which would predict infinite IQ loss at the boundary of zero exposure — a nonsense result. ln(BPb + 1) gives ΔIQ = 0 at BPb = 0, satisfying the physically required boundary condition. Crump 2013 fits the data with this form precisely for this reason; the tool's implementation enforces it in both the Python backend and the JavaScript client mirror.

Facility-attributable computation

Crump's β was estimated against total blood lead in the cohort studies. A child living near a ULAB facility carries a non-zero background BPb from food, soil, paint, water, and other non-facility sources — a substantial fraction of the global child population has baseline BPb in the 3–7 µg/dL range (IHME, GBD 2023). The tool models the IQ deficit attributable to the facility's contribution on top of that background as the marginal effect on Crump's dose-response:

ΔIQ_attributable = f(BPb_background + ΔBLL_facility) - f(BPb_background)

For the log specification this expands to:

ΔIQ_attributable = β × [ ln(bg + ΔBLL + 1) - ln(bg + 1) ]
                  = β × ln( (bg + ΔBLL + 1) / (bg + 1) )

For the linear specification the background term cancels (β × (bg + ΔBLL) − β × bg = β × ΔBLL), so the linear-form prediction is independent of background.

The default BPb_background is 3.0 µg/dL — a defensible central estimate for global LMIC childhood populations per IHME GBD 2023. The BLL Impacts tab exposes a slider so users can raise the background to ~5–7 µg/dL for higher-burden settings (sub-Saharan Africa, South Asia industrial regions) or drop it to 0 to reproduce Crump's raw dose-response. Crump's Table 5 values are reproduced exactly when bg = 0.

Per receptor the tool stores:

delta_iq_central(receptor) = β × [ln(bg + ΔBLL_total + 1) - ln(bg + 1)]
delta_iq_lower(receptor)   = β_ci_lower × [...]
delta_iq_upper(receptor)   = β_ci_upper × [...]

where ΔBLL_total(receptor) is the facility-attributable BLL increment from Section 5.4. Crump returns negative values for IQ loss; the heat map displays the magnitude as positive "IQ points lost" (<0.5 trace, 0.5–1 minimal, 1–3 low, 3–5 moderate, 5–10 high, >10 severe). Hover readouts show the central estimate with the CI range inline, rounded to one decimal place — Crump's ±60% CI on β does not support tighter display precision.

Why the marginal-effect framing matters

An earlier implementation of this layer applied Crump's dose-response by treating ΔBLL_facility as if it were the child's total BPb — effectively assuming BPb_background = 0. Because the log dose-response is concave (steeper at low BPb, flatter at high BPb), plugging a 10 µg/dL facility increment in at BPb = 0 predicts a larger IQ deficit than the same increment added on top of a 3 µg/dL background. For a typical informal LMIC scenario, the corrected marginal-effect math gives roughly half the IQ loss of the bare-substitution math at near-source receptors. The marginal formulation is the correct attribution framing because Crump's β is itself a curve, not a per-µg/dL constant.

Caveats

• Concurrent BPb is a temporal measure, not a spatial one. Crump's "concurrent" refers to the BPb measured at the same time as the IQ test. Applying this dose-response to a spatial receptor field implicitly assumes steady-state exposure: interpret ΔIQ at a receptor as the IQ deficit a child would experience if their chronic BPb equalled bg + ΔBLL_total(receptor). Alternative temporal measures (peak, lifetime average) live in the backend's coefficient table for sensitivity analysis but are not exposed in the production UI.
• Below total BPb = 5 µg/dL the prediction is extrapolative. Crump 2013 notes that the dose-response's statistical significance becomes marginal at low BPb; the underlying cohort data are sparse below ~5 µg/dL. Predictions at low-BPb receptors (often the far-field) carry larger relative uncertainty than the central estimate suggests. With the default background of 3 µg/dL, low-ΔBLL receptors still sit within the cohort's calibration range as long as ΔBLL pushes total BPb above ~5.
• Source cohorts are US, European, Mexican, and Australian children. Applying the dose-response to LMIC contexts (West Africa, South Asia) assumes the biological dose-response between BPb and IQ is transportable across populations. This is a defensible default — the biokinetic and neurological mechanisms are universal — but it has not been empirically validated in LMIC contexts. Population differences in nutritional status, age distribution, and concomitant exposures may shift the dose-response in ways the CI band does not capture.
• Background BLL is a single scalar. Real backgrounds vary spatially, demographically, and temporally — children in a peri-urban subsistence settlement may have very different baseline BPb from children in a regional capital. The current implementation uses one user-supplied scalar across the entire receptor grid. A future revision could plumb a per-receptor background field from a population-BPb raster.
• No population weighting. The map shows ΔIQ per receptor, not summed over the people who live there. Two facilities with the same per-receptor ΔIQ field can produce very different total community IQ losses depending on local population density. Population-weighted summary statistics are planned but not yet wired.
• Concavity at high ΔBLL. The log dose-response is concave: in the upper part of its validity range, doubling exposure adds progressively fewer IQ points. The heat map has limited resolving power for distinguishing severities at the upper end of the valid BLL range, even before the validity threshold is crossed.
• ΔIQ suppressed past biokinetic model validity. Crump 2013 is not evaluated on receptors with ΔBLL_total ≥ 30 µg/dL — these are in state 3 of the four-state classification (section 5.4). The receptor's delta_iq_* fields are returned as null and the IQ map renders them with an off-scale hatched marker rather than a numeric value. This is intentional: the dose-response cannot honestly be applied to a BLL the underlying biokinetic model cannot predict.

Reference

Crump KS, Van Landingham C, Bowers TS, Cahoy D, Chandalia JK (2013). A statistical reevaluation of the data used in the Lanphear et al. (2005) pooled-analysis that related low levels of blood lead to intellectual deficits in children. Critical Reviews in Toxicology 43(9): 785–799. DOI: 10.3109/10408444.2013.832726.

Background BLL default: Institute for Health Metrics and Evaluation (2024). Global Burden of Disease Study 2023 — Lead Exposure Estimates. https://ghdx.healthdata.org/record/ihme-data/gbd-2023-lead-exposure-estimates-1990-2023.

5.7 LMIC validation gap

The IEUBK anchor values used in this tool are derived from US and European child populations. Validation in LMIC populations near ULAB recycling facilities is limited. Several factors may shift the underlying biokinetic relationships in these contexts:

• Differences in baseline nutritional status (calcium, iron, vitamin D status affect Pb absorption).
• Concomitant exposures (paint, cosmetics, foodware) that may shift the BLL baseline and saturation regime.
• Bioavailability of speciated lead in ULAB-affected soils, which has not been characterised in detail.

Pure Earth's research agenda includes population BLL surveillance in ULAB-affected communities to refine these anchor values. Until that work is published, BLL predictions in this tool should be interpreted as screening estimates indicating the relative magnitude of facility-attributable exposure, not as quantitative individual risk assessments.

5.8 References

Key methodology references for this section (full citations in Section 7):

• US EPA IEUBK Guidance Manual (1994) — biokinetic model used to derive both pathway anchor tables.
• Richmond-Bryant et al. (2014) — re-evaluation of EPA IEUBK uptake parameters.
• Lanphear et al. (2005) — pooled analysis of low-level lead exposure cohort studies.
• Crump et al. (2013) — Lanphear reanalysis with refined confounder treatment; supplies the ΔIQ dose-response used in Section 5.5. Crit Rev Toxicol 43(9): 785–799.
• CDC Blood Lead Reference Value Update (2021) — current 3.5 µg/dL reference value.

6. Scope: what ULAB Plume predicts, and what it does not

This section is the consolidated statement of ULAB Plume's scope. Every individual prediction the tool surfaces falls within the "Predicts" column; questions in the "Does not predict" column require methodologies or data the tool does not currently incorporate.

Additional limitations of the predicted quantities:

• Meteorology: Synthetic AERMET-format met derived from STAR-format wind rose climatology, not real hourly observations. See section 2.2 for the derivation pipeline and reproducibility properties.
• Validation: Determinism, mass-balance, and integration tests cover the dispersion-to-receptor pipeline (see tests/test_parser_integration.py and the cross-comparison documented in section 2.5). External validation against ambient measurement campaigns at documented smelter sites is a planned future addition.
• Spatial resolution: 50 m minimum receptor distance; sub-50 m exposures cannot be characterised. Preview-mode's coarse near-source spacing under-resolves coarse-bin deposition (section 2.4).
• Source resolution: Single-stack + co-located fugitive AREA source only. Multi-stack facilities require running each stack as a separate scenario and superposing.
• Terrain and surface: Flat terrain assumed (no AERMAP integration); open-country surface roughness (no urban-canyon effects). Both are reasonable defaults for typical LMIC peri-urban / industrial siting but should be flagged in dense urban or hilly contexts.
• BLL pathways: Inhalation + soil/dust ingestion + foliar-crop deposition. The crop pathway is opt-in on the BLL surface via the "Include crops" checkbox; the response always carries both excluding-crop and including-crop totals plus their validity flags. Drinking water, lead paint, occupational exposures, and dietary uptake from non-aerial sources (including root uptake of historical soil Pb) are excluded — these are aggregated implicitly into the user-supplied background BLL.
• Demographic variation: A single population-average dose-response (IEUBK + Crump 2013) is used. Susceptibility variation by nutritional status, age, and concomitant exposures is not modelled at the receptor level.
• Food-chain pathways: A foliar-deposition crop pathway (leafy + cereal classes) is modelled at screening precision and surfaced as two heat map layers plus an optional contribution to ΔBLL — see section 5.5. Root uptake (soil-to-plant transfer of historical soil Pb via the roots) is not modelled. The crop-to-BLL conversion is a single linear slope (EFSA 2010), which over-predicts ΔBLL at very high dietary intakes — the validity guardrail is the 30 µg/dL BLL ceiling from section 5.4. Dietary uptake from non-aerial sources (animal products, fish, water) is implicitly aggregated into the user-supplied background BLL.

Foundational-model validity. ULAB Plume composes three published models: AERMOD (dispersion), IEUBK (biokinetic), and Crump et al. 2013 (ΔBLL → ΔIQ dose-response). The validity ranges and limitations within each model's predicted range are accepted as foundational and are not separately enumerated here. Users assessing whether a specific ULAB Plume output should be cited as authoritative for a given context should consult the source models' validation documentation: US EPA AERMOD Implementation Guide (2024), US EPA IEUBK Guidance Manual (1994), and Crump et al. 2013 (Critical Reviews in Toxicology 43(9): 785–799).

7. References

• US EPA, AERMOD Implementation Guide (2024). EPA-454/B-19-035.
• US EPA, AERSCREEN User's Guide (2024). EPA-454/B-21-005.
• US EPA, AP-42, Chapter 12.16: Secondary Lead Processing (1998).
• US EPA, Guidance Manual for the Integrated Exposure Uptake Biokinetic Model for Lead in Children (1994). EPA/540/R-93/081.
• US EPA, Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK) (2007).
• US EPA, Adult Lead Methodology — Recommendations of the Technical Review Workgroup for Lead (2003). OSWER 9285.7-54.
• Brunekreef, B. (1984). "The relationship between air lead and blood lead in children: a critical review." Science of the Total Environment 38: 79–123.
• Hilts, S.R. (2003). "Effect of smelter emission reductions on children's blood lead levels." Science of the Total Environment 303(1–2): 51–58.
• Richmond-Bryant, J., Meng, Q., Davis, A., et al. (2014). "Influence of exposure measurement errors on results from epidemiologic studies of different designs." Journal of Exposure Science & Environmental Epidemiology 24: 624–634.
• Lanphear, B.P., Hornung, R., Khoury, J., et al. (2005). "Low-level environmental lead exposure and children's intellectual function: an international pooled analysis." Environmental Health Perspectives 113(7): 894–899.
• WHO (2021). WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. https://www.who.int/publications/i/item/9789240034228
• CDC, Blood Lead Reference Value Update (2021).
• Manhart, A. & Adjei, F. (2026). Lead-Acid Battery Recycling in Selected African Settings: Status Quo and Considerations for Sector Policies. CGD Policy Paper 387, Center for Global Development. https://www.cgdev.org/publication/lead-acid-battery-recycling-selected-african-settings-statusquo-and-considerations
• Stanaway, J.D. et al., "Global, regional, and national disability-adjusted life-years for 359 diseases and injuries…" JAMA, 2026.
• Brown, S.L. (2008). UK Food Standards Agency systematic review of food-crop Pb transfer fractions; foliar pathway composite-R framework.
• European Commission Joint Research Centre (2003). Technical Guidance Document on Risk Assessment. EUR 20418 EN; Part II covers food-crop deposition transfer.
• US EPA Office of Solid Waste and Emergency Response (2002). Multimedia, Multipathway, and Multireceptor Risk Assessment (3MRA) Protocol for Hazardous Waste Combustion Facilities. Source of the leafy-crop composite R = 0.10 central anchor.
• Chamberlain, A.C. (1970). "Interception and retention of radioactive aerosols by vegetation." Atmospheric Environment 4(1): 57–78. Original empirical basis for cereal R = 0.01–0.05 on smooth grain surfaces.
• Liu Y. et al. (2020). Paired soil/crop Pb measurements at the Jiyuan TS lead smelter (Henan, China). Primary cereal R empirical tuning reference: D ≈ 350 mg/m²/yr, observed grain Pb 0.12–0.48 mg/kg.
• Liu et al. (2022). Jiaozuo smelter paired-measurement study. Second cereal R validation point: D ≈ 300 mg/m²/yr, grain Pb 0.62 mg/kg.
• EFSA Panel on Contaminants in the Food Chain (CONTAM) (2010). Scientific opinion on lead in food. EFSA Journal 8(4): 1570. Source of the child dietary BLL slope (β_diet = 0.105 µg/dL per µg/day). DOI: 10.2903/j.efsa.2010.1570
• Carrington, C.D. et al. (2019). FDA Interim Reference Level (IRL) framework for child dietary lead exposure. Cross-reference for the BLL slope applied to the 7-year, 22.6 kg default receptor.
• FAO (2022). FAOSTAT — global crop yield statistics. Source of cereal edible-grain yield defaults (wheat 3.4 t/ha global; rice 4.8 t/ha Asia, 6.5 t/ha Americas).
• European Commission (2023). Commission Regulation (EU) 2023/915 of 25 April 2023 on maximum levels for certain contaminants in food. Official Journal of the European Union L 119/103.

8. Version history

Calibration of default values

The tool's default emission factors, PSD distributions, and stack-geometry hints are intended to track the best published evidence available for LMIC ULAB recycling. Updates to those defaults — like the 1.1 recalibration above against Manhart & Adjei 2026 — change the tool's starting point for screening but do not change the validation status of the underlying physics: AERMOD execution, met synthesis, parsing, and bilinear interpolation are unchanged. The pending validations documented in Section 2.5 (comparison vs ambient measurements, vs reanalysis-driven AERMOD runs, and vs alternate dispersion models) remain pending and apply equally to the new defaults.

Users supplying site-specific values via the wizard (custom throughput, control efficiency, stack geometry) bypass the defaults entirely; the calibration above governs only the quick-scenario presets and any field the user leaves at its default.

9. Contact

For methodology questions, validation collaboration, or to report discrepancies in tool output:

Chris Kinally, Pure Earth — chrisk@pureearth.org

This document is a living methodology and will be updated as the tool evolves. The latest version is always available at https://ulabplume.org/methodology.html.