Air pollution dynamics in arid urban-industrial zones for environmental engineering management

Air pollution assessment in arid and rapidly urbanizing regions is challenged by complex source interactions, strong meteorological influences, and limited monitoring infrastructure. In Jordan, conventional air quality studies often rely on annual averages and sector-specific analyses, limiting their ability to capture spatiotemporal variability, climate-driven dispersion, and source heterogeneity. This thesis addresses these gaps by advancing a unified, multidisciplinary framework that integrates meteorological analysis, urban and industrial air quality monitoring, wastewater-based indicators, atmospheric dispersion modeling, vehicular emissions modeling, and machine learning techniques to support evidence-based air quality management. The primary objectives of this research were to: (i) evaluate the impacts of COVID- 19 lockdown measures on air quality in Amman and Az Zarqa; (ii) assess the heterogeneous effects of seasonal variation and meteorological parameters on PM₁₀ dispersion in major industrial estates; (iii) develop innovative and cost-effective methodologies for forecasting key air pollutants using routinely monitored wastewater parameters; (iv) construct comprehensive emission inventories and dispersion assessments for major wastewater treatment plants (WWTPs); and (v) enhance vehicular emissions modeling frameworks under arid and urban conditions. Analysis of air quality data from 2018–2023 revealed that COVID-19 lockdown measures resulted in substantial reductions of 60–80% in H₂S, SO₂, and NO₂ and 20–40% in CO and PM₁₀ in Amman and Az Zarqa, primarily driven by decreased traffic volume and industrial activity, with temperature, humidity, and wind speed exerting temporally varying influences on pollutant dispersion. In two major industrial estates: Abdullah II Ibn Al Hussein Industrial Estate and Al Masane’ Estate, seasonal and meteorological analyses showed that temperature dominated PM₁₀ variability before the pandemic, combined temperature humidity effects were significant during lockdown periods, and wind speed became the primary dispersion driver post-pandemic, with low wind regimes promoting pollutant accumulation. Seasonal PM₁₀ in Jordan was adjusted using ERA5 wind, precipitation, and temperature data via a regression model. Spring showed the highest PM₁₀ from strong winds, summer the lowest from weak winds and high temperatures. Adjusted values highlight meteorological effects and help separate them from local emissions, improving seasonal air quality assessment. The study that develops a cost-effective framework to forecast H₂S and NO₂ at Jordanian wastewater plants using routine effluent parameters, showing strong site- and year-specific correlations (H₂S–COD r up to 0.99; NO₂–NO₃⁻ r up to 1.0). Multiple models including MLR, Random Forest, and a hybrid LSTM–XGBoost were tested, with LSTM–XGBoost achieving the highest accuracy (R² > 0.90 for NO₂, R² > 0.85 for H₂S; RMSE menor 1.1 ppb) and capturing seasonal peaks effectively. This approach enables reliable, timely air quality forecasting, reducing reliance on expensive sensors and supporting targeted pollution mitigation strategies. Emission inventory and dispersion modeling of the Al-Baqa’a and As Samra WWTPs (2018–2023) identified extreme H₂S concentrations reaching 177 ppb, significant industrial contributions to ambient SO₂ and NO₂, methane emissions contributing approximately 1,230 tCO₂e annually, and PM2.5 concentrations up to 4.7 times background levels in nearby communities, while mitigation strategies achieved emission reductions of up to 89%. Enhanced vehicular emissions modeling incorporating climate-responsive silt loading and urban geometry corrections significantly improved prediction accuracy for PM₁₀, PM2.5, CO, and NO₂ along major highways. The US EPA AP-42 and European EEA algorithms were refined using region-specific parameters, including highway geometry, climate-driven dynamic silt loading, and fuel density correction, and applied to two highways in Jordan. The improved models reduced normalized differences by 60–77% for PM₁₀ and PM₂.₅ and by 72% for CO, demonstrating strong gains in predictive accuracy. The framework also captured distinct emission regimes, including a seasonal silt-loading peak of ~17.5 g/m² in autumn at the industrial site. Although NO₂ improvements were modest (4–40%) due to complex photochemical processes, the approach proved robust for air quality assessment in arid cities. This thesis establishes a robust and transferable framework for air pollution assessment, forecasting, and mitigation in arid and climate-sensitive regions. By integrating pandemic-scale natural experiments, industrial meteorological analysis, wastewater-based indicators, advanced dispersion modeling, vehicular emissions modeling, and machine learning, the research provides practical tools and scientific evidence to support targeted air quality management, emissions control, and public health–oriented environmental policymaking in rapidly urbanizing environments. RESUMEN La evaluación de la contaminación atmosférica en entornos áridos y de rápida urbanización enfrenta importantes desafíos debido a la complejidad de las fuentes emisoras, la fuerte influencia meteorológica y la limitada capacidad de monitoreo. En Jordania, los estudios tradicionales suelen basarse en promedios anuales y análisis sectoriales, lo que restringe la comprensión de la variabilidad espaciotemporal, la dispersión climática y la heterogeneidad de las fuentes. Esta tesis aborda estas limitaciones mediante un enfoque unificado y multidisciplinar que integra análisis meteorológicos, monitoreo urbano e industrial, indicadores derivados de aguas residuales, modelización de dispersión atmosférica, emisiones vehiculares y técnicas de aprendizaje automático orientadas a la gestión ambiental basada en evidencia. Los objetivos principales fueron: (i) evaluar los efectos del confinamiento por COVID-19 sobre la calidad del aire en Ammán y Az Zarqa; (ii) analizar la variabilidad estacional y meteorológica del PM en grandes polígonos industriales; (iii) desarrollar métodos innovadores y de bajo coste para prever contaminantes clave mediante parámetros de aguas residuales; (iv) elaborar inventarios y modelos de dispersión para las principales plantas de tratamiento (WWTP); y (v) mejorar los modelos de emisiones vehiculares bajo condiciones áridas y urbanas. El análisis de datos de 2018-2023 mostró descensos del 60-80% en HS, SO2 y NO2 y del 20-40% en CO y PM10 durante el confinamiento, debidos principalmente a la reducción del tráfico y la actividad industrial. Temperatura, humedad y viento influyeron de forma variable en la dispersión. En los polígonos AbdullahII IbnAlHussein y AlMasane, la temperatura fue dominante antes de la pandemia; la interacción temperaturahumedad durante el confinamiento; y la velocidad del viento después, con acumulaciones bajo regímenes débiles. Los ajustes estacionales con datos ERA5 mostraron niveles máximos de PM10 en primavera y mínimos en verano, permitiendo separar efectos meteorológicos de emisiones locales. El modelo predictivo basado en aguas residuales para H2S y NO2 mostró altas correlaciones (HS-COD r=0.99; NO2-NO3 - r= 1.0). El modelo híbrido LSTMXGBoost alcanzó la mayor precisión (R2>0.90 para NO2; R2> 0.85 para H2S; RMSE menor 1.1ppb), reproduciendo adecuadamente los picos estacionales. Esta metodología ofrece predicciones fiables sin recurrir a sensores costosos y refuerza las estrategias de mitigación. La modelización de emisiones en las WWTP de AlBaqaa y As Samra (20182023) detectó concentraciones extremas de H2S (177ppb), importantes aportes industriales de SO2 y NO2, emisiones de metano (~1230 tCOe anuales) y niveles de PM2.5. hasta 4,7 veces superiores al fondo. Las medidas correctoras redujeron las emisiones hasta un 89%. La mejora de los modelos de emisiones vehiculares, incorporando la carga de polvo dependiente del clima y la geometría vial, aumentó significativamente la precisión para PM10, PM2.5, CO y NO2. Las adaptaciones de los modelos EPA-AP42 y EEA redujeron las diferencias en 6077% para partículas y 72% para CO. Se detectó un pico otoñal de carga de polvo (~17,5g/m) en zonas industriales. Aunque las mejoras en NO2 fueron menores (4-40%), el modelo mostró alta robustez para contextos urbanos áridos. En conjunto, la tesis establece un marco integral, transferible y económico para evaluar, prever y mitigar la contaminación atmosférica en regiones áridas y sensibles al clima, aportando herramientas científicas y evidencia aplicada para fortalecer la gestión ambiental, el control de emisiones y la salud pública en ciudades de rápido crecimiento.