# Roadside PM10 concentrations in relation to traffic volume and meteorological conditions in Makassar, Indonesia
> Asis S.N.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105050678962
Jurnal / Konferensi: Ecological Engineering and Environmental Technology
Tahun terbit: 2026
DOI: https://doi.org/10.12912/27197050/230933
ISSN: 27197050
Kuartil SJR: Q3
Citations: 0

## Authors
- Asis S.N.

## Abstract
This study aimed to quantify the combined influence of traffic activity and meteorological conditions on roadside PM₁₀ in Makassar, Indonesia, and to evaluate whether an integrated model provides better predictive performance than traffic-only and nonlinear alternatives.Field measurements were conducted at 30 roadside points between 25 February and 20 March 2025.PM₁₀ concentrations were measured using a high volume air sampler, traffic was represented by total vehicle volume, and temperature, relative humidity, air pressure, wind direction, and wind velocity were recorded concurrently.The analysis included the Shapiro-Wilk test, Pearson correlation, simple linear regression, multiple linear regression, fifth-order polynomial regression, Random Forest benchmarking, leave-one-out cross-validation, residual diagnostics, Moran's I, and exploratory inverse distance weighting.Total vehicle volume was positively associated with PM₁₀ (r = 0.752, p < 0.001), while relative humidity showed a strong negative association (r = -0.740,p < 0.001).The final multiple linear regression model using total vehicle volume and relative humidity achieved R² = 0.720 and adjusted R² = 0.699.Under leave-one-out cross-validation, it outperformed the other models with RMSE = 24.79µg/m³, MAE = 18.62 µg/m³, cross-validated R² = 0.650, and observed-predicted r = 0.807.Random Forest ranked second but did not surpass the multiple linear model.No significant global spatial autocorrelation was detected in measured PM₁₀ or model residuals, and IDW showed weak out-of-location predictive skill, so the spatial surface was retained only as an exploratory visualization.The study is limited by 30 point-level observations, selected one-hour monitoring periods, and the absence of external seasonal validation.Practically, the model provides an interpretable screening framework for identifying roadside locations where traffic intensity and humidity jointly indicate elevated PM₁₀.Its originality lies in combining traffic, meteorology, internal validation, robustness diagnostics, a machine-learning benchmark, and explicitly constrained spatial interpretation within a single roadside assessment framework for Makassar.

## Keywords
- Environmental science
- Volume (thermodynamics)
- Relation (database)
- Meteorology
- Atmospheric sciences
- Traffic volume
- Remote sensing
- Air pollution
- Particulates
- Noise (video)

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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