Air Pollution

Air pollution harms health and climate. Learn pollutants (PM₂.₅, PM₁₀, SO₂, NOx, CO, O₃), sources and emission estimates, meteorology and dispersion (Gaussian plume, stack height), air-quality standards and AQI, and control devices (cyclones, ESPs, bag filters, scrubbers, SCR).

Air pollution causes millions of premature deaths each year. Emissions from vehicles, power plants, industry, construction dust and burning biomass combine with weather to create unhealthy air.

Engineers estimate emissions, predict how they disperse, and design control equipment and policies to keep concentrations within standards.

An analogy

A chimney plume is like ink released into a flowing river: it drifts downwind and spreads out, getting more dilute the farther and more turbulent the flow.

Pollutants and effects

Particulate matter (PM₁₀, PM₂.₅ — lungs and heart), SO₂ and NOx (respiratory, acid rain, secondary particles), CO (blood oxygen), ground-level ozone (formed from NOx + VOCs in sunlight), lead and air toxics. Greenhouse gases (CO₂, CH₄) drive climate change.

Meteorology and dispersion

Wind speed and atmospheric stability control dilution. Unstable (sunny) conditions mix vigorously; stable conditions and inversions trap pollution near the ground (winter smog). Tall stacks and plume rise increase effective height and reduce ground-level concentrations.

Control technologies

Particulates: cyclones (coarse), electrostatic precipitators (99 %+), fabric (bag) filters (99.9 %), wet scrubbers. Gases: flue-gas desulphurisation (limestone scrubbers), low-NOx burners and SCR, catalytic converters for vehicles, VOC incineration/adsorption.

Air-quality management

Monitoring networks, AQI communication, emission inventories and dispersion models (AERMOD, CALPUFF, CMAQ) support policy: fuel standards (BS-VI/Euro 6), stack emission limits, clean-air action plans, dust control on construction sites and clean cooking fuels.

Formulas

• Emission estimate: E = Activity × Emission factor × (1 − control efficiency)
• Gaussian plume (ground-level centreline): C = Q/(π σ_y σ_z u) · exp(−H²/(2σ_z²))
• ESP (Deutsch–Anderson): η = 1 − exp(−w A / Q)
• SO₂ from fuel sulphur: SO₂ = 2 × S burned (mass)

Worked example: Gaussian plume estimate

Given: Q = 100 g/s, u = 5 m/s, H = 50 m; At distance where σ_y = 80 m, σ_z = 40 m

Find: Ground-level centreline concentration.

• Q/(πσ_yσ_zu) = 100/(π × 80 × 40 × 5) = 1.99 × 10⁻³ g/m³
• × exp(−2500/3200) = × 0.458
• C = 9.1 × 10⁻⁴ g/m³ = 911 µg/m³

Answer: ≈ 911 µg/m³ (short-term)

Worked example: Electrostatic precipitator

Given: Drift velocity w = 0.1 m/s; Plate area 5000 m², gas flow 200 m³/s

Find: Efficiency.

• η = 1 − exp(−0.1 × 5000/200) = 1 − e^(−2.5) = 91.8 %

Answer: ≈ 91.8 % (more plate area needed for 99 %)

Common mistakes

• Wrong: Relying on tall stacks instead of control. — Right: Dilution isn't removal — control emissions at source.
• Wrong: Ignoring secondary pollutants. — Right: SO₂/NOx form fine particles and ozone downwind.

Applications

• Power & industry: ESPs, bag filters, FGD and SCR on stacks; continuous emission monitoring.
• Urban air quality: Traffic management, clean fuels, dust control and monitoring networks.
• EIA & permitting: Dispersion modelling to show compliance with standards.

Key takeaways

• Know the pollutants and their sources.
• E = A × EF × (1 − η).
• Gaussian plume: C falls with height and dispersion.
• Control at source: ESP, bag filters, FGD, SCR.

Going further

• Stability classes: Pasquill–Gifford classes A–F set σ_y, σ_z curves for plume models.
• Receptor modelling: Source apportionment (PMF, CMB) identifies which sources cause measured pollution.
• Indoor air: Cooking fuels and ventilation drive large health impacts indoors.