This review paper deals with the field of dust generation on gravel roads, dust suppressant performance and evaluation techniques. By applying the proper dust suppressant, matching the gravel road condition specific to the site, dust emission can be reduced, thereby providing a healthier ambient air environment, increasing road safety and ride comfort while reducing the need and cost of vehicle repair, road maintenance activities, and aggregate replacement. By applying the proper application rate of the dust suppressant, the cost of annual dust control as well as the environmental impact can be significantly reduced. Suitable measuring techniques for evaluating dust suppressant efficiency will facilitate the choice of the most appropriate dust suppressant and its optimal application rate.
Due to oxidation, breakdown, and leaching, dust suppressants will be lost from the gravel road surface. Methods for residual dust suppressant concentration supervision are a valuable tool for estimating life-length and optimal application rates, and, hence, efficiency of different products. The objective of this study was to identify methods for quantitative analyses of lignosulphonate and chloride, develop and adapt the methods for application on a gravel matrix, and validate the methods using samples collected in-situ. Results strongly suggest that the reliability and repeatability of the developed methods (23 % for lignosulphonate and 30 % for chloride, respectively) are acceptable for determination of relative variations in residual concentrations of dust suppressed gravel wearing courses.
Maintaining gravel roads is crucial, as loose gravel poses safety risks and increases vehicle costs. Current methods used by the Swedish road administration, Trafikverket, are subjective and time-consuming. Road agencies need a cost-effective, efficient, and unbiased approach to assess gravel road conditions. Studies show human ratings are error-prone and inconsistent. This study aims to develop an automatic method for estimating loose gravel using audio recordings from inside a vehicle, capturing the sound of gravel hitting the car's bottom. These recordings were classified into four classes based on Trafikverket regulations. Sound features were extracted and analysed using supervised machine-learning methods. The Multilayer Perceptron (MLP) achieved the highest classification accuracy of 0.96, with an F1 score, recall, and precision of 0.97. Results indicate that audio data can effectively classify loose gravel conditions.