<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Predicting Near-Road Exposure from Vehicular Emission in Urban Areas Using the LSTM Technique</title>
      <link>https://trid.trb.org/View/2645436</link>
      <description><![CDATA[Emissions arising from vehicular traffic are a significant contributor to air pollution in urban environments, causing severe health effects in pedestrians and near-road residents. Thus, it is crucial to estimate pollutant concentrations along the roadside in urban areas. However, machine learning models such as random forest (RF) and support vector regression (SVR) often fail to consider the time dependency pattern in air quality models. Thus, this study forecast pollutant concentrations from vehicular emission using the long short-term memory (LSTM) method at urban traffic intersections. Furthermore, shorter monitoring intervals of 5 min were considered to account for dynamic traffic flow and atmospheric changes. The pollutants considered in this study were CO, NO₂, PM2.5, and PM10, estimated from traffic volume data categorized by vehicle type and the existing environmental conditions. The results indicated that shorter intervals provided satisfactory results in forecasting vehicular pollution. Moreover, better prediction accuracy was achieved using LSTM than with RF and SVR. The 𝑅² value varied between 0.78 and 0.94, and mean absolute error (MAE) and root mean square error (RMSE) were less than 9 and 13  𝜇ℊ/m³, respectively. The findings of the study indicate that the proposed LSTM model accurately predicts the concentrations arising from vehicular activities. Furthermore, assessing models over shorter periods effectively captured the variation in pollution trends. This work illustrates the significance of integrating deep learning techniques with experimental monitoring studies to investigate traffic-related air pollution in urban areas.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645436</guid>
    </item>
    <item>
      <title>Microphysical Modeling &amp; Analysis of ACCESS 2 Aviation Exhaust Observations</title>
      <link>https://trid.trb.org/View/2688769</link>
      <description><![CDATA[As the popularity of and global access to air travel expands, quantifying its impact on climate and air pollution becomes increasingly important. However, the spatial-temporal distribution of aircraft emissions and their byproducts span many orders of magnitude, as contrail development begins within seconds, but can spread to the kilometer-scale and persist for hours. The wide range of spatial and temporal scales makes it difficult to obtain detailed knowledge of the composition and evolution of emissions from measurements or modeling studies alone. In an attempt to increase understanding, this project simulates the short-term, near- and far-field evolution of aircraft exhaust aerosol and contrail particles and gases with two computer models: GATOR-GCMOM and an LES model. Together, these two models simulate phenomena spanning a spatial range from millimeters to thousands of kilometers. Detailed microphysical processes in the models are validated and improved with field measurements from NASA’s ACCESS-2 campaign, so that the models may provide more credible estimates of impacts on climate and atmospheric composition when used at the regional and global scale.]]></description>
      <pubDate>Wed, 22 Apr 2026 10:45:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688769</guid>
    </item>
    <item>
      <title>Coastal city pollution from time-varying traffic emissions: a high-resolution WRF-CFD comparison of dynamic sea-land breeze and static prevailing wind</title>
      <link>https://trid.trb.org/View/2652594</link>
      <description><![CDATA[Rapid urbanization has intensified traffic-related air pollution in street networks, particularly in coastal cities frequently affected by sea-land breeze (SLB) meteorology. Conventional air-quality assessments commonly adopt simplified steady-state ‘prevailing wind’ assumptions, failing to capture the dynamic and diurnal evolution of SLB circulations. This methodological simplification can weaken the effectiveness of pollution-mitigation strategies or even make them counterproductive. To address this limitation, a high-temporal-resolution WRF-CFD coupled model is employed to integrate time-evolving SLB meteorological fields with time-varying traffic emissions, assessing the pollutant dispersion under the SLB and summer prevailing wind (SPW) conditions. The results show that under SLB conditions, street-level pollutant concentrations become decoupled from traffic emission patterns, exhibiting opposing trends during morning and evening rush hours compared to the predictable behavior under SPW. Weak morning land breezes hinder pollutant dispersion, increasing concentrations by 1.4 times compared to SPW, whereas strong evening sea breezes enhance ventilation, reducing concentrations by 43%. Moreover, the midday collision of sea and land breezes generates a low-ventilation ‘convergence zone’, causing severe pollution episodes even during off-peak traffic hours. During this convergence period, the average pollution concentration under SLB is over 1.4 times higher than during SPW, with peak concentrations reaching nearly twice those of SPW. Although daily average concentrations are similar under both weather conditions, the SLB-induced convergence effect can cause short-term rapid pollutant accumulation, significantly amplifying pedestrian exposure risks. Consequently, for air quality assessment in coastal cities, the main findings show that SLB-induced meteorological dynamics (e.g., the midday convergence) can be a more critical determinant of acute pollution events than traffic volume itself, challenging the conventional prevailing steady-state assumption. The developed framework also provides an essential tool for designing meteorology-responsive dynamic traffic management and street-level air quality alert systems, enabling targeted control strategies under different weather conditions to reduce exposure risks.]]></description>
      <pubDate>Fri, 03 Apr 2026 12:12:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652594</guid>
    </item>
    <item>
      <title>Study Supporting the Evaluation of Directive (EU) 2016/2284 on the Reduction of National Emissions of Certain Atmospheric Pollutants (NEC Directive)</title>
      <link>https://trid.trb.org/View/2633326</link>
      <description><![CDATA[The National Emissions reduction Commitments Directive (2016/2284, NEC Directive) is one of the key pillars of air pollution control legislation in the EU, and must be reviewed by the European Commission by December 2025. This study supported the Commission in the evaluation, providing targeted support in certain areas. The specific objectives were to: carry out a comprehensive stakeholder consultation and analyse and synthesize the data gathered; review the scope and relevance regarding emissions from specific sectors, i.e., agriculture, maritime traffic and aviation; assess the state of implementation and relevance of the emission reduction measures set out in Part 2 of Annex III to the Directive across the EU and assess overall coherence of the Directive with EU agricultural policy; and to contribute to the analysis of the costs and benefits of the Directive, with a particular focus on the assessment of administrative burden and identifying possible unnecessary costs and options for simplification. The study gathered evidence from a range of sources to support analysis against the evaluation questions across the five Better Regulation Guidelines criteria of effectiveness, efficiency, relevance, coherence and EU value added.]]></description>
      <pubDate>Wed, 01 Apr 2026 11:47:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633326</guid>
    </item>
    <item>
      <title>“Dust and wheels”: Unraveling urban traffic–PM₂.₅ coupling in arid cities</title>
      <link>https://trid.trb.org/View/2676254</link>
      <description><![CDATA[Traffic emissions can dominate near-road exposure to fine particulate matter (PM₂.₅) in arid cities with limited atmospheric dispersion. Using multi-source hourly observations of traffic activity, air pollutants, and meteorology from the Yinchuan Urban Ecosystem Research Station, we characterized traffic–PM₂.₅ coupling by combining correlation networks with an interpretable machine-learning framework (Random Forest, RF; partial dependence analysis; SHapley Additive exPlanations, SHAP). Pollutant concentrations exhibited clear diurnal cycles: PM₂.₅, black carbon (BC), and nitrogen dioxide (NO₂) increased at night and in the early morning. The RF model identified BC, NO2, and relative humidity (RH) as the strongest predictors of PM₂.₅. Nonlinear effects and interactions indicated higher PM2.5 risk under humid, low-wind conditions indicative of stagnation. SHAP interaction analysis showed strengthened joint contributions for BC × RH and BC × NO₂, suggesting that BC-oriented controls may yield greater co-benefits under humid conditions. These findings provide a scientific basis for traffic-emission control and targeted air-pollution mitigation strategies.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676254</guid>
    </item>
    <item>
      <title>How far can we improve urban air quality and population exposure by changing mobility? An analysis in Paris</title>
      <link>https://trid.trb.org/View/2612075</link>
      <description><![CDATA[Atmospheric pollution still is a major sanitary problem in urban areas. Road traffic remains an important source of pollutant emissions, and it is strongly influenced by urban mobility. This study investigates the influence of five ambitious mobility scenarios in Paris to reduce pollutant emissions, concentrations and outdoor population exposure to regulated pollutants (NO₂, PM₂.₅), and emerging ones (ultrafine particles (UFP) and black carbon (BC)), using a multi-scale modeling approach. The mobility scenarios suppose: reductions and renewal of utility vehicles fleet; reductions and renewal of passenger cars fleet; a widespread practice of home-office in Île-de-France region; an electrification of vehicle fleet; and a replacement of particular and utility vehicle fleet to soft mobility. In general, all scenarios reduced pollutant emissions, concentrations and outdoor population exposure for the analyzed pollutants, but with different levels of efficiency. Replacing particular and utility vehicles to soft mobility reduced the most UFP concentrations (up to 43%), and the electrification of vehicles led to the highest reductions for NO₂ (up to 75%). Similar reductions on BC were observed with these two scenarios (up to 50%). However, no scenario was effective to strongly reduce PM₂.₅ concentrations (up to 16%), pointing out the importance to also reduce emissions from sources other than traffic in the region. Reducing/renewing particular vehicle fleet was more effective than utility vehicles to reduce pollutant concentrations. The widespread home-office practice proved a small, but non-negligible effect to reduce concentrations for NO₂ and BC, similar to those obtained by reducing/renewing utility vehicles.]]></description>
      <pubDate>Fri, 19 Dec 2025 10:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612075</guid>
    </item>
    <item>
      <title>Vehicle emission models and their applications in China: A comprehensive review</title>
      <link>https://trid.trb.org/View/2633588</link>
      <description><![CDATA[Road transport becomes the dominant driver behind the increase in global transport energy consumption, and consequently, vehicle emissions are a primary contributor to global climate change and atmospheric pollution. Despite extensive research into vehicle emissions, significant challenges persist in the refinement and application of emission models. Here, we review key techniques for developing emission factors and categorize vehicle emission models into macroscopic, mesoscopic, microscopic, and machine learning-based models. Specifically, we emphasize that an increasing number of scholars have been developing deep learning-based vehicle emission models owing to the rapid advancement of deep learning technology. We summarize the usages of vehicle emission models to compile emission inventories and discuss their applications in urban site selection, environmental pollution control, traffic management, and policy formulation. However, variations exist among vehicle emission models in terms of modeling accuracy, applicability, and computational complexity. Thus, future research should address the following challenges: (1) development of localized emission models that are suitable for different regions and scenarios; (2) fusion of deep learning-based emission models with physical models to develop novel models; (3) coupling of emission models with traffic simulation models to tackle urban sustainable transportation issues; (4) formulation of transportation policies for sustainable urban development.]]></description>
      <pubDate>Tue, 09 Dec 2025 09:18:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633588</guid>
    </item>
    <item>
      <title>Clean Air in Cities: Impact of the Layout of Buildings in Urban Areas on Pedestrian Exposure to Ultrafine Particles From Traffic</title>
      <link>https://trid.trb.org/View/2606530</link>
      <description><![CDATA[Traffic-related pollutant concentrations are typically much higher in near-roadway microenvironments, and pedestrian and resident exposures to air pollutants can be substantially increased by the short periods of time spent on and near roadways. The design of the built environment plays a critical role in the dispersion of pollutants at street level; after normalizing for traffic, differences of a factor of ~5 have been observed between urban neighborhoods with different built environment characteristics. The authors examined the effects of different built environment designs on the concentrations of street-level ultrafine particles (UFP) at the scale of several blocks using the Quick Urban and Industrial Complex (QUIC) numerical modeling system. The model was capable of reasonably reproducing the complex ensemble mean 3D air flow patterns and pollutant concentrations in urban areas at fine spatial scale. The authors evaluated the effects of several built environment designs, changing building heights and spacing while holding total built environment volumes constant. The authors found that ground-level open space reduces street-level pollutant concentrations. Holding volume/surface area constant, tall buildings clustered together with larger open spaces between buildings resulted in substantially lower pollutant concentrations than buildings in rows. Buildings arranged on a ‘checkerboard’ grid with smaller contiguous open spaces, a configuration with some open space on one of the sides of the roadway at all locations, resulted in the lowest average concentrations for almost all wind directions. Rows usually prohibit mixing for perpendicular and oblique wind directions, even when there are large spaces between them, and clustered buildings have some areas where buildings border both sides of the roadways, inhibiting mixing. The model results suggest that pollutant concentrations drop off rapidly with height in the first 10 m or so above the roadways. In addition, the simulated vertical concentration profiles show a moderate elevated peak at the roof levels of the shorter buildings within the area. Model limitations and suggestions both for urban design are both discussed.]]></description>
      <pubDate>Mon, 08 Dec 2025 15:19:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606530</guid>
    </item>
    <item>
      <title>Shipping emissions forecast in the Iberian Peninsula for 2050 impact on air quality, mortality and related costs</title>
      <link>https://trid.trb.org/View/2611453</link>
      <description><![CDATA[The International Transport Forum projected that global demand for transport could triple by 2050, with shipping demand likely increasing even more rapidly. However, most scenarios for the next 20-30 years suggest that current regulatory policies may be insufficient to offset the rise in traffic. Thus, this study evaluates the impacts of shipping emissions on air quality, health, and external costs in the Iberian Peninsula, analyzing past (2017), current (2022), and projected future (2050) scenarios. Using high-resolution emissions data, it was assessed the influence of existing regulations, including the IMO 2020 sulphur cap and a hypothetical implementation of the shore-side electricity (SSE) in the major ports of the Iberian Peninsula, and project future impacts under a business-as-usual scenario for 2050. Results indicate significant improvements in air quality from the IMO sulphur cap, with reductions in NO2, SO2, and particulate matter (PM2.5 and PM10) concentrations, mainly near the Strait of Gibraltar. SSE implementation showed localized air quality benefits near ports, although a slight O3 increase was verified due to atmospheric interactions (NOx titration). PM2.5 shipping related emissions exposure resulted in approximately 1 061 premature deaths and 3 626 years of life lost (YLL) between 2017 and 2022, with economic costs of 9.1 billion for the Iberian Peninsula. Projections for 2050 suggested that although the fleet efficiency is expected to increase and Tier III compliance mitigate emissions, the rising population density and aging will likely lead to more premature deaths and YLL, totaling 4 459 deaths and 68 081 YLL, with costs reaching 53.2 billion. According to these results, regulatory actions, including potential Atlantic Ocean ECA implementation, are recommended to offset the combined effects of demographic changes and shipping traffic growth in the future.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611453</guid>
    </item>
    <item>
      <title>Spatial and temporal mapping of transport emissions and application of air quality models using low cost sensor data</title>
      <link>https://trid.trb.org/View/2614763</link>
      <description><![CDATA[Traffic-related atmospheric emissions of greenhouse gases (GHG) and toxic air pollutants (AP) are a serious environmental problem that affects climate change and air quality in megacities. About 80 % of air pollution in São Paulo comes from vehicles. This work aimed to develop a methodology using a traffic demand model for GHG and AP inventories of vehicular emissions and demonstrate its applicability to the Metropolitan Area of São Paulo (MASP) as a part of regional air quality and climate change modelling. These high-resolution emission inventories also allow identifying hot spots of air pollution and poor air quality with a spatial resolution of 0.5 km and temporal resolution of 1 h. With this, we also intend to develop an approach for the validation of the emission model through low cost sensor measurements. These sensors will be placed through the MASP close to the identified vehicle emission hot spots to continuously measure over one-year duration to address a novel question on how the low-cost sensors data can be applied for improving the model performance and air quality monitoring. This paper integrates two approaches: the vehicle emission and air quality modeling and the use of low-cost sensors for model validation and develop novel approaches for high-resolution spatial mapping. This work provided a basis for establishing sound climate change policies in other areas such as public health and urban planning. These high-resolution emission inventories also allowed identifying hot spots of air pollution and poor air quality with a spatial resolution of 0.5 km and temporal resolution of 1 h. Data from sensors NOTS were compared with reference data obtained from the Osasco monitoring network website and data from devices at other nearby air quality monitoring stations. This comparison made it possible to determine the errors for adjusting the calibration model in the field.The calibration of the NOTS platforms considered the co-location between the NOTS devices and the CETESB monitor platform.]]></description>
      <pubDate>Wed, 12 Nov 2025 08:39:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614763</guid>
    </item>
    <item>
      <title>Ammonia emission from real-world in-use vehicle fleets in a megacity in China - based on tunnel measurement</title>
      <link>https://trid.trb.org/View/2611429</link>
      <description><![CDATA[Ammonia (NH3) has been widely recognized as a key precursor of atmospheric secondary aerosol formation. Vehicle emission is a major source of urban atmospheric NH3. With the tightening of emission standards and the growing trend of vehicle fleet electrification, it is imperative to update the emission factors for NH3 from real-world on-road fleets. In this study, a tunnel measurement was conducted in the urban area of Tianjin, China. The fleet-average NH3 emission factor (EF) was 11.2 mg/(km.veh), significantly lower than those in previous studies, showing the benefit of emission standard updating. Through a multiple linear regression analysis, the EFs of light-duty gasoline vehicles, light-duty diesel vehicles, and heavy-duty diesel vehicles (HDDVs) were estimated to be 5.7 +/- 0.6 mg/(km.veh), 40.8 +/- 5.1 mg/(km.veh), and 160.2 +/- 16.6 mg/(km.veh), respectively. Based on the results from this study, the authors found that HDDVs, which comprise <3 % of the total vehicles may contribute approximately 22 % of total NH3 emissions in Tianjin. The results highlight NH3 emissions from HDDVs, a previously potentially overlooked source of NH3 emissions in urban areas. The actual on-road NH3 emissions from HDDVs may exceed current expectations, posing a growing concern for the future.]]></description>
      <pubDate>Mon, 27 Oct 2025 09:34:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611429</guid>
    </item>
    <item>
      <title>Electric bus adoption for sustainable urban mobility: Public policies, technological maturity of cities, and small business innovation</title>
      <link>https://trid.trb.org/View/2583339</link>
      <description><![CDATA[The Brazilian National Energy Balance underscores transportation as the primary energy consumer and major contributor to atmospheric pollution. Despite escalating energy demand, Brazil relies heavily on fossil fuels, and its energy efficiency lags behind other nations. Electric buses offer a viable solution, boasting low greenhouse gas emissions and various socio-economic and environmental benefits, including noise reduction, enhanced air quality, cost savings, and job creation in the electric vehicle sector. Small business innovation is pivotal in this context, driving the development of technologies and solutions. The research inquiry focuses on the impact of public policies on the technological maturity of electric bus adoption for sustainable urban mobility in Brazilian cities. Employing a multi-method approach, including the Analytical Hierarchy Process (AHP) and interviews with industry stakeholders, preliminary findings highlight the critical role of regulatory frameworks and incentives in enhancing cities' maturity levels, surpassing technological advancements and performance metrics in importance.]]></description>
      <pubDate>Tue, 09 Sep 2025 08:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2583339</guid>
    </item>
    <item>
      <title>Study on non-exhaust emissions in road transport</title>
      <link>https://trid.trb.org/View/2569575</link>
      <description><![CDATA[More than 96% of the European population was exposed to PM2.5 concentrations exceeding the World Health Organization’s annual air quality guideline limit in 2022.Road transport is a primary source of particulate matter in urban areas. As exhaust emissions decrease, non-exhaust emissions (NEEs) – particles from brake, tyre and road wear – are now a dominant source of urban particulate matter (PM). London served as the core case study to assess policy and technical interventions to reduce NEE. A simulation using the London Atmospheric Emissions Inventory (LAEI 2019) baseline was run to estimate projected particulate matter (PM) emissions through to 2050. Several scenarios were modelled and evaluated using a cost-benefit analysis for scenarios one, two, and three assuming only technical interventions and using a benefit analysis for scenarios four, five, and six, which explore changes related to travel behaviour and vehicle choice. The benefit analysis revealed that shifting travel behaviour from cars to public transport delivers up to five times greater PM emission reductions than fleet electrification alone (excluding the benefits of electrification for exhaust emissions).]]></description>
      <pubDate>Thu, 26 Jun 2025 13:31:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569575</guid>
    </item>
    <item>
      <title>Experimental investigation of the effects of JP8 and amorphous elemental boron additives on combustion characteristics for i-DSI engine</title>
      <link>https://trid.trb.org/View/2552339</link>
      <description><![CDATA[The combustion of conventional fossil fuels releases greenhouse gases into the atmosphere, contributing to atmospheric pollution and climate change. The use of alternative fuels in internal combustion engines represents a significant step towards mitigating these issues. In this study, the effects of JP8, a fuel used in space, missile, rocket, and aviation applications, and mixtures of amorphous elemental boron (AEB) on combustion were tested using a commercial i-DSI engine with dual spark ignition. Tests were conducted by blending gasoline with 0% (G100), 10% (JP8_10), 20% (JP8_20), 30% (JP8_30), and 40% (JP8_40) JP8 by mass. Additionally, new mixtures were tested by adding 2% AEB by mass to each gasoline-JP8 fuel (G100_2AEB, JP8_10_2AEB, JP8_20_2AEB, JP8_30_2AEB, JP8_40_2AEB). Performance metrics such as torque, power, mean pressure, specific fuel consumption, volumetric efficiency, and exhaust temperature, as well as emissions of CO, CO2, UHC, and NOx, were measured. The maximum performance torque from the engine catalog is 119?Nm at 2800?rpm, while the tests measured it at 105.84?Nm. Torque, power, and mean pressure, which are functions of each other, increased by 2.239% from G100 to JP8_40 compared to gasoline, and this increase was 5.125% for JP8_10_2AEB. Specific fuel consumption decreased by 5.424% with JP8_40 fuel and by 9.199% with JP8_10_2AEB fuel compared to gasoline. CO emissions decreased by 38.187% with JP8_40_2AEB fuel compared to gasoline. CO2 emissions increased by 34.456% with JP8_40_2AEB fuel. UHC emissions decreased by 57.589% with JP8_30_2AEB fuel. NOx emissions increased by 118.036% with JP8_40_2AEB fuel, while increasing by 124.861% with JP8_40 fuel.]]></description>
      <pubDate>Tue, 17 Jun 2025 09:58:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2552339</guid>
    </item>
    <item>
      <title>Quantifying the Volume of Particulate Matter at Bus Stations</title>
      <link>https://trid.trb.org/View/2535016</link>
      <description><![CDATA[Congested urban traffic substantially contributes to air pollution in cities. While waiting at bus stops, passengers may be exposed to increased contamination caused by vehicles, including particulate matter (PM). The modern bus stop layout, position and design ignore air quality and allow excessive exposure to pollution. Particulate matter seriously harms the environment, threatening human health and severely damaging all living organisms. The research purpose is to monitor particle emissions at the bus station in the city of Žilina (Slovakia), amassing data on exhaust emissions released from buses at the station premises. As moving or running-engine vehicles incessantly produce atmospheric emissions, the authors measure air quality during peak hours at the bus station. The results indicate a direct interconnection between passing vehicles and produced particle emissions, when multiple times higher emission levels are revealed. During the morning rush hour, the particulate matter exceeded 360% for PM2.5 and 420% for PM10. The research showed PM released directly from the buses tends to accumulate in covered premises of the bus station, severely damaging the health of passengers and staff. The authors' study warns about possible risks of deteriorating human health as waiting passengers unknowingly inhale contaminated particles. The authors' results indicate the largest emission producers and suggest remedial measures.]]></description>
      <pubDate>Wed, 07 May 2025 15:54:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2535016</guid>
    </item>
  </channel>
</rss>