Air Pollution: Emissions, Atmospheric Dispersion and Control

Running case. An industrial district reports odour and particulate complaints near a school and a wetland.

Unit product. Develop an emission inventory, monitoring layout, simplified dispersion sensitivity analysis and control shortlist.

Unit orientation

This unit asks what can and cannot be concluded from observations in the running case. Begin by listing state variables and the time basis, identifying which quantities have been measured and which are assumptions. The engineering task is to distinguish evidence of transport, transformation, dilution and removal, and to prevent an untested model from becoming a safety claim. Compare at least two approaches with the same functional requirements, and include one sensitivity or uncertainty test in the written submission.

5.1 Air contaminants and emission inventories

Ambient air pollution combines primary emissions with secondary pollutants formed through atmospheric chemistry. Particulate matter is defined by aerodynamic size ranges, not by chemical composition; gaseous species behave differently. Stack concentration and ambient concentration are different quantities. A low concentration at a tall stack may still correspond to a large emission mass rate if volumetric flow is high.

Construct an emission inventory with fuel use, activity, emission factor, capture efficiency and control-system uptime. Separate routine emissions from start-up, maintenance and upset events. Local background, meteorology and topography can dominate the receptor measurement; a source-attribution claim requires data rather than proximity alone.

Governing relationship and units

Emission rate = activity rate × emission factor × (1−control efficiency).

Worked context

A boiler with 4,000 kg fuel/day and illustrative 2 g/kg particulate factor emits 8 kg/day before control. A tested 95% mass removal would reduce this estimate to 0.4 kg/day; measurement uncertainty and operating conditions still matter.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.2 Dispersion and source–receptor relationships

Atmospheric transport depends on wind, turbulence, stability, terrain and release characteristics. The Gaussian plume is a conceptual steady-state model under idealized conditions, not an adequate replacement for a regulatory dispersion assessment. Changes in mixing height or stable nighttime conditions can produce counterintuitive ground-level patterns.

When using an illustrative plume calculator, state wind direction, receptor height, source elevation, stack exit effects and dispersion parameterization. Evaluate crosswind displacement and how the centerline maximum moves with effective source height. The US EPA AERMOD system uses far more complete meteorological and terrain treatment and evolves through documented revisions.

Governing relationship and units

C(x,0,0)≈[Q/(πuσ_yσ_z)]exp(−H²/(2σ_z²)), under idealized reflection and steady wind.

Worked context

A high effective source height can reduce immediate near-field ground concentration but does not destroy emissions. A valley or changing wind regime may invalidate flat-terrain assumptions. Real permits require the jurisdictionally adopted model and data.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.3 Emission controls and removal verification

Cyclones, fabric filters, electrostatic precipitators, absorbers and adsorption beds remove different pollutants through different mechanisms. A cyclone relies on particle inertia and will not reliably remove a soluble gas simply because both pollutants share a stack. Adsorption involves finite capacity and eventual breakthrough; wet scrubbers generate wastewater and sometimes sludge.

Describe the target species and particle size, upstream temperature, humidity, pressure drop, corrosion risk and expected control availability. Distinguish equipment design removal efficiency from measured whole-process emission reduction. A bypassing filter can meet an equipment catalogue rating while an actual stack test shows poor overall capture.

Governing relationship and units

η = 1 − C_out/C_in for equal volumetric basis, with concentration measurements corrected to the same conditions.

Worked context

At matched stack flow and reference conditions, an inlet particulate concentration of 150 mg/m³ and outlet of 7.5 mg/m³ implies 95% removal. If flows differ, mass rates should be compared instead.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.4 Monitoring and exposure interpretation

A monitoring network must answer an explicit question: compliance at a regulatory receptor, worker protection, community exposure or source characterization. Low-cost optical sensors need humidity correction and careful calibration. Time averaging matters because short spikes can be obscured by daily averages, and instrument detection limits may affect conclusions near background levels.

Plan the location of upwind and downwind monitors, data recovery targets, calibration, blank checks and meteorological measurements. A high reading during construction cannot automatically be attributed to a particular site without wind data, activity records and an appropriate comparison station. Communicate uncertainty and the relevant averaging duration clearly.

Governing relationship and units

Time-weighted mean = Σ(C_iΔt_i)/ΣΔt_i.

Worked context

Three hourly measurements of 15, 45 and 30 µg/m³ average 30 µg/m³, but a missing hour during the highest-emission period could bias the average downward. A completeness rule is therefore part of the result.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

Synthesis and field decision

In the case scenario, develop an emission inventory, monitoring layout, simplified dispersion sensitivity analysis and control shortlist. For each quantity used, identify its spatial representativeness, time basis, uncertainty and the responsible person who can confirm the value. Explicitly document how the plan changes during equipment failure or extreme conditions.

Unit assessment

1. For air pollution: emission, transport, control and measurement, define a control volume and list all stocks, transports and reaction terms with units.
2. Explain how different sampling times or locations could reverse the conclusion in this scenario: An industrial district reports odour and particulate complaints near a school and a wetland.
3. Choose two uncertainties and quantify which has the larger effect on the recommendation. Show a reproducible parameter table.
4. Use one of the four governing relationships to calculate a low, central and high case, then explain why a numerical result is not a permit limit.
5. Design an independent field or bench experiment that discriminates between a transport limitation and a reaction limitation.
6. Write a corrective-action plan for equipment failure or a surprising monitoring result; identify response time and independent verification.
7. Construct a single-page public explanation of the issue without concealing unresolved uncertainty.
8. Critique a proposal that reports percent removal but omits residual streams, peak-flow capacity and monitoring quality.

Assessment policy: A numerical answer without units, documented assumptions, and interpretation is incomplete. Explain what additional observation or regulation would be needed to authorize real-world action.

Unit 5: Air pollution: emission, transport, control and measurement

Running case. An industrial district reports odour and particulate complaints near a school and a wetland.

Unit product. Develop an emission inventory, monitoring layout, simplified dispersion sensitivity analysis and control shortlist.

Unit orientation

This unit asks what can and cannot be concluded from observations in the running case. Begin by listing state variables and the time basis, identifying which quantities have been measured and which are assumptions. The engineering task is to distinguish evidence of transport, transformation, dilution and removal, and to prevent an untested model from becoming a safety claim. Compare at least two approaches with the same functional requirements, and include one sensitivity or uncertainty test in the written submission.

5.1 Air contaminants and emission inventories

Ambient air pollution combines primary emissions with secondary pollutants formed through atmospheric chemistry. Particulate matter is defined by aerodynamic size ranges, not by chemical composition; gaseous species behave differently. Stack concentration and ambient concentration are different quantities. A low concentration at a tall stack may still correspond to a large emission mass rate if volumetric flow is high.

Construct an emission inventory with fuel use, activity, emission factor, capture efficiency and control-system uptime. Separate routine emissions from start-up, maintenance and upset events. Local background, meteorology and topography can dominate the receptor measurement; a source-attribution claim requires data rather than proximity alone.

Governing relationship and units

Emission rate = activity rate × emission factor × (1−control efficiency).

Worked context

A boiler with 4,000 kg fuel/day and illustrative 2 g/kg particulate factor emits 8 kg/day before control. A tested 95% mass removal would reduce this estimate to 0.4 kg/day; measurement uncertainty and operating conditions still matter.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.2 Dispersion and source–receptor relationships

Atmospheric transport depends on wind, turbulence, stability, terrain and release characteristics. The Gaussian plume is a conceptual steady-state model under idealized conditions, not an adequate replacement for a regulatory dispersion assessment. Changes in mixing height or stable nighttime conditions can produce counterintuitive ground-level patterns.

When using an illustrative plume calculator, state wind direction, receptor height, source elevation, stack exit effects and dispersion parameterization. Evaluate crosswind displacement and how the centerline maximum moves with effective source height. The US EPA AERMOD system uses far more complete meteorological and terrain treatment and evolves through documented revisions.

Governing relationship and units

C(x,0,0)≈[Q/(πuσ_yσ_z)]exp(−H²/(2σ_z²)), under idealized reflection and steady wind.

Worked context

A high effective source height can reduce immediate near-field ground concentration but does not destroy emissions. A valley or changing wind regime may invalidate flat-terrain assumptions. Real permits require the jurisdictionally adopted model and data.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.3 Emission controls and removal verification

Cyclones, fabric filters, electrostatic precipitators, absorbers and adsorption beds remove different pollutants through different mechanisms. A cyclone relies on particle inertia and will not reliably remove a soluble gas simply because both pollutants share a stack. Adsorption involves finite capacity and eventual breakthrough; wet scrubbers generate wastewater and sometimes sludge.

Describe the target species and particle size, upstream temperature, humidity, pressure drop, corrosion risk and expected control availability. Distinguish equipment design removal efficiency from measured whole-process emission reduction. A bypassing filter can meet an equipment catalogue rating while an actual stack test shows poor overall capture.

Governing relationship and units

η = 1 − C_out/C_in for equal volumetric basis, with concentration measurements corrected to the same conditions.

Worked context

At matched stack flow and reference conditions, an inlet particulate concentration of 150 mg/m³ and outlet of 7.5 mg/m³ implies 95% removal. If flows differ, mass rates should be compared instead.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

5.4 Monitoring and exposure interpretation

A monitoring network must answer an explicit question: compliance at a regulatory receptor, worker protection, community exposure or source characterization. Low-cost optical sensors need humidity correction and careful calibration. Time averaging matters because short spikes can be obscured by daily averages, and instrument detection limits may affect conclusions near background levels.

Plan the location of upwind and downwind monitors, data recovery targets, calibration, blank checks and meteorological measurements. A high reading during construction cannot automatically be attributed to a particular site without wind data, activity records and an appropriate comparison station. Communicate uncertainty and the relevant averaging duration clearly.

Governing relationship and units

Time-weighted mean = Σ(C_iΔt_i)/ΣΔt_i.

Worked context

Three hourly measurements of 15, 45 and 30 µg/m³ average 30 µg/m³, but a missing hour during the highest-emission period could bias the average downward. A completeness rule is therefore part of the result.

Reasoning exercise. Using the running case, make a two-column table: available observations and missing evidence. State a plausible parameter range with units, identify one observation capable of disproving the principal assumption, and explain how the engineering action would change if a measured input were doubled. Do not treat the worked illustrative numbers as calibration data.

Model critique. Check dimensional consistency before calculation. If data come from different dates, locations, temperature bases or chemical definitions, reconciliation is required before drawing a conclusion. Report the sensitivity of the decision to uncertain inputs and distinguish the physical result from any legal or public-health judgment.

Synthesis and field decision

In the case scenario, develop an emission inventory, monitoring layout, simplified dispersion sensitivity analysis and control shortlist. For each quantity used, identify its spatial representativeness, time basis, uncertainty and the responsible person who can confirm the value. Explicitly document how the plan changes during equipment failure or extreme conditions.

Unit assessment

1. For air pollution: emission, transport, control and measurement, define a control volume and list all stocks, transports and reaction terms with units.
2. Explain how different sampling times or locations could reverse the conclusion in this scenario: An industrial district reports odour and particulate complaints near a school and a wetland.
3. Choose two uncertainties and quantify which has the larger effect on the recommendation. Show a reproducible parameter table.
4. Use one of the four governing relationships to calculate a low, central and high case, then explain why a numerical result is not a permit limit.
5. Design an independent field or bench experiment that discriminates between a transport limitation and a reaction limitation.
6. Write a corrective-action plan for equipment failure or a surprising monitoring result; identify response time and independent verification.
7. Construct a single-page public explanation of the issue without concealing unresolved uncertainty.
8. Critique a proposal that reports percent removal but omits residual streams, peak-flow capacity and monitoring quality.

Assessment policy: A numerical answer without units, documented assumptions, and interpretation is incomplete. Explain what additional observation or regulation would be needed to authorize real-world action.