Review Reports

Reviewer’s Comment

Authors’ Response

1. Data sources and resolution: The methodology lacks crucial details regarding data sources and their spatial and temporal resolution. What satellite products and meteorological data were used? Were the satellite-derived precipitation data validated against ground-based measurements?

We appreciate this important observation. Section 2.2 “Data sources and preprocessing” has been expanded, and a new Table 1 has been added. It lists all data categories (traffic, road geometry, building data, meteorology, and satellite data), their sources, temporal and spatial resolutions, and their role in the modeling framework. We also clarify that IMERG V06 satellite-derived precipitation data were validated against ground-based measurements from the Jordan Meteorological Department (R² = 0.82, RMSE = 0.7 mm day⁻¹), ensuring reproducibility.

2. Dynamic silt Loading model: The model’s development, calibration, and assumptions lack detail. The use of temperature as a proxy for wind requires justification.

Section 2.3.3 “Dynamic silt loading model” has been substantially expanded. The governing equation (Eq. 5) now defines all parameters and boundary conditions, with a clear explanation of the least-squares optimization used for calibration. The physical rationale for constants (k_{\text{acc}}) and (k_{\text{wash}}) is discussed. We justify using temperature as a proxy for wind based on strong seasonal correlation with Shamal wind events in arid Jordanian regions, and this limitation is now explicitly acknowledged.

3. Variable consistency (Lines 185–196): Check that all variables correspond precisely with those in the equations.

All equations and variables within the stated range have been thoroughly checked. Each symbol is now explicitly defined within or immediately after the equations to ensure internal consistency.

4. Contextual discussion for Figure 3: Add background on the monitoring site, observation period, and key trends.

Figure 3 is now properly introduced in Section 2.3.3 as representing the “regional climatology” that informs the silt loading model. We include a description of observation sites, the period (2016–2023), and key trends in temperature and precipitation relevant to the modeling framework.

5. Misplaced methodology paragraphs (Lines 252–276): Move these paragraphs to the methodology section.

The indicated content has been relocated and integrated into Sections 2.3.1–2.3.3, consolidating all methodological details in one coherent section. The “Results” section now focuses exclusively on findings and interpretation.

6. Low resolution of Figure 4:The figure is difficult to interpret.

Figure 4 has been regenerated at 300 dpi resolution with an updated caption and clear labeling. The text now confirms the figure’s improved quality.

7. Incorrect Equation 7:

Equation 7 has been corrected in Section 2.4, Step 2. All related computations were verified for accuracy.

8. Redundant caption in Figure 5:

The Figure 5 caption has been simplified to: “Daily time series of modeled silt loading (SL) from 2016 to 2023.”

All redundant explanatory text was removed; interpretation now appears only in the main text.

9. Poor visualization in Figure 6:The King Abdullah Hwy trend is not clear; separate plots are recommended.

Figure 6 was revised into two subplots with independent vertical scales, making the King Abdullah Hwy trend clearly distinguishable. The corresponding description in the text was updated.

Editorial note: Minor consistency and formatting checks recommended.

We conducted a complete review of table/figure numbering and removed residual formatting artifacts. A final proofreading was performed to ensure consistency and readability.

Reviewer Comment

Author Response

1. Clarification of PM Emission Equations (Section 2.3.1)– Explain the rationale behind using (I)(T_f/H) instead of a simple I/H.– Clarify the meaning of the constant “3” in (W/3)^1.5.– Cite AP-42 or supporting literature to justify the equation form.

The expression (I)(T_f/H) was used to integrate both geometric confinement (I/H) and traffic intensity effects (T_f), representing the cumulative influence of flow-induced turbulence and street canyon trapping on PM emissions. This coupling enhances realism in urban conditions where both factors are interdependent. The constant “3” in (W/3)^1.5 represents an empirical normalization derived from the average lane width and reference vehicle spacing in AP-42 (U.S. EPA, 1995, Section 13.2.1), ensuring unit consistency with the original fugitive dust formulation. The equation structure and parameters are consistent with AP-42 methodologies and adapted for urban canyon modifications following the approach of (Ruda Sarria et al. 2025).

2. Traffic Data Acquisition and Processing (Section 2.2)– Specify number and distribution of cameras.– Identify periods affected by COVID-19 and % missing data.– Describe interpolation method and justification.

Traffic data were collected from 10 fixed-location traffic cameras covering major intersections and mid-segments of both highways. Approximately 8.5% of 2020 data were missing due to COVID-19 restrictions (March–May). Missing hourly data were gap-filled using linear temporal interpolation, which provided the lowest error (RMSE = 2.1 vehicles min⁻¹) compared with spline and nearest-neighbor methods. This approach maintained temporal integrity without distorting traffic flow variability.

3. Quantification of Secondary NO₂ Formation (Section 3.3)– Add VOC inventory and estimate secondary NO₂ contribution.– Compare industrial vs. urban segments.

We appreciate the reviewer’s constructive suggestion. To address this point, we added the following paragraph: “To assess the influence of photochemical processes on NO₂ concentrations, a VOC emission inventory was compiled for the Army Highway corridor using data from the Jordanian Ministry of Environment (2022). The estimated total VOC emissions from adjacent industrial facilities were approximately 320 tonnes year⁻¹, dominated by aromatic hydrocarbons (38%), alkenes (24%), and oxygenated compounds (15%). These species are known precursors to secondary NO₂ via atmospheric oxidation pathways. Assuming a conservative secondary NO₂ yield ratio of 0.3 from reactive VOC oxidation (Stirnweis et al. 2017), the additional NO₂ formation potential was estimated at 0.8–1.2 µg m⁻³, which accounts for 12–18% of the model–measurement difference observed during peak traffic hours. This finding indicates that secondary chemical production likely contributes to the model’s underestimation in the industrial zone. Spatially, the model exhibited higher performance along the King Abdullah Highway (urban segment; R² = 0.82, RMSE = 4.3 µg m⁻³) compared with the Army Highway (industrial segment; R² = 0.74, RMSE = 5.7 µg m⁻³). The greater residual bias along the Army Highway aligns with the distribution of industrial VOC sources, suggesting that photochemical secondary NO₂ formation plays a significant role in local air quality variability.”

4. Validation of Dynamic Silt Loading Model (Section 3.4)– Add comparison between modeled and observed peak SL values.

We appreciate the reviewer’s constructive suggestion. To address this point, we added a quantitative comparison between modeled and observed peak monthly SL values for the Army Highway (section 3.4). The revised text now highlights the model’s performance in reproducing both the magnitude and timing of the highest SL events. A new table (Table 6) summarizes these results, confirming that the dynamic model accurately captures the seasonal variation patterns.

5. Figure 4 Compliance with Journal Guidelines– Add pollutant labels and units to all panels.

Figure 4 has been updated. Each panel now includes clear pollutant labels (PM₁₀, PM₂.₅, CO, NO₂) and corresponding y-axis units (ton/yr). The figure caption was also revised for clarity and compliance with journal formatting standards.

Reviewer’s Comment

Author’s Response

1. Many cited publications are inconsistent with the article (e.g., Ref. 13 on social media). Review all citations—likely AI-generated errors.

We appreciate the reviewer’s careful attention. We conducted a comprehensive manual audit of all references to ensure accuracy, relevance, and consistency with the manuscript’s focus on vehicular emissions, atmospheric processes, and environmental modeling. All irrelevant or incorrect citations (e.g., Ref. 13) were removed and replaced with peer-reviewed, domain-appropriate sources. An acknowledgment was added: “The authors thank the reviewers for identifying errors in the initial reference list, which have been corrected following a full manual audit.”

2. The model does not physically distinguish between direct (exhaust) and indirect (non-exhaust) emissions—all PM values are linearly summed.

We agree. The model’s scope has now been clarified in Section 2.3.1, which explicitly states that the modified AP-42 equations estimate non-exhaust PM emissions (road dust resuspension). Exhaust PM is not included in this framework. The section now reads: “The modified AP-42 equations (1) and (2) are used to estimate non-exhaust PM emissions from road dust resuspension… Exhaust PM from engine combustion is not explicitly calculated.”

3. Statistical evidence—no t-test or R² for individual models.

We acknowledge this oversight. A paired t-test (and Wilcoxon test when necessary) was applied to compare model residuals (measured–modeled) between the old and modified versions for each pollutant and highway. The corresponding p-values have been added in a new column in Table 3. Results indicate statistically significant improvements (p < 0.05) for all pollutants except NO₂ on the Army Highway.

4. Validation results are illogical.

We thank the reviewer for this important observation. A new paragraph was added in Section 2.4, clarifying that a simplified box model was used for validation to enable first-order performance comparison without full atmospheric dispersion modeling. The Discussion (Section 3.3) now explains that the smaller improvement at the industrial site is due to unaccounted secondary photochemistry, which affects NO₂ formation and validates the observed behavior.

5. Review the equations used again.

We reviewed all equations carefully and added a specific reference to US EPA AP-42, Chapter 13.2.1, as the methodological basis. The rationale behind the introduced I/H (width-to-height) ratio and dynamic silt loading parameterization is now explicitly described, emphasizing their relevance to arid-urban environments.

6. Figure 4 is unreadable and overloaded. Only 4–6 examples should be included.

We fully agree. The previous 16-panel figure was replaced with a 4-panel composite figure highlighting representative results: (A) PM₁₀ – Army Highway, (B) PM₁₀ – King Abdullah Highway, (C) CO – Army Highway, and (D) NO₂ – Army Highway. This revision aligns with journal readability standards and presents the key findings clearly.