Abstract. Motor vehicle road traffic in central Budapest was reduced by approximately 50 % of its ordinary level for several weeks as a consequence of various limitation measures introduced to mitigate the first outbreak of the COVID-19 pandemic in 2020. The situation was utilised to assess the real potentials of urban traffic on air quality. Concentrations of NO, NO2, CO, O3, SO2 and particulate matter (PM) mass, which are ordinarily monitored in cities for air quality considerations, aerosol particle number size distributions, which are not rarely measured continuously on longer runs for research purposes, and meteorological properties usually available were collected and jointly evaluated in different pandemic phases. The largest changes occurred over the severest limitations (partial lockdown in the Restriction phase from 28 March to 17 May 2020). Concentrations of NO, NO2, CO, total particle number (N6–1000) and particles with a diameter < 100 nm declined by 68 %, 46 %, 27 %, 24 % and 28 %, respectively, in 2020 with respect to the average reference year comprising 2017–2019. Their quantification was based on both relative difference and standardised anomaly. The change rates expressed as relative concentration difference due to relative reduction in traffic intensity for NO, NO2, N6–1000 and CO were 0.63, 0.57, 0.40 and 0.22 (%/%), respectively. Of the pollutants which reacted in a sensitive manner to the change in vehicle circulation, it is the NO2 that shows the most frequent exceedance of the health limits. Intentional tranquillising of the vehicle flow has considerable potential for improving the air quality. At the same time, the concentration levels of PM10 mass, which is the most critical pollutant in many European cities including Budapest, did not seem to be largely affected by vehicles. Concentrations of O3 concurrently showed an increasing tendency with lower traffic, which was explained by its complex reaction mechanism. Modelling calculations indicated that spatial gradients of NO and NO2 within the city became further enhanced by reduced vehicle flow.
During the simulation of the urban heat island phenomenon, the accurate representation of urban geometry in numerical models is crucial. In this study, the local climate zone (LCZ) system was incorporated into the Weather Research and Forecasting (WRF) model in order to facilitate proper land surface information for the model integrations. After the calculation of necessary input canopy parameters, based on local static datasets, simulations were performed to test the model's performance in predicting nearsurface air temperature (T a) and urban heat island intensity (ΔT) under a heatwave period in July 2017. The modelled values were evaluated against the observations of the local urban climate monitoring system. The results suggest that WRF with a single-layer canopy scheme and the LCZ-based static database was able to capture the spatiotemporal variation of the aforementioned variables reasonably well. The daytime T a was generally overestimated in each LCZ. At nights, slight overestimations (underestimations) occurred in LCZ 6, LCZ 9, and LCZ D (LCZ 2 and LCZ 5). The mean ΔT was underestimated in the night-time; however, the daytime ΔT was estimated accurately. The mean maxima (minima) of ΔT were underestimated (overestimated) with around 1.5-2°C, particularly in LCZ 2 and LCZ 5. Some components of the surface energy budget were also computed to shed light on the inter-LCZ differences of T a. It was concluded that the nocturnal ground heat flux was about five times higher in urban LCZs than in the rural LCZ D, which resulted in a reduced cooling potential over the urbanized areas. Keywords Weather Research and Forecasting model. Single-layer urban canopy scheme. Local climate zones. Urban heat island. Szeged (Hungary)
Abstract. Motor vehicle road traffic in central Budapest was reduced by approximately 50 % of its ordinary level for several weeks as a consequence of various limitation measures introduced to mitigate the first outbreak of COVID-19 pandemic in 2020. The situation was utilised to assess the real potentials of urban traffic on air quality. Concentrations of NO, NO2, CO, O3, SO2 and particulate matter (PM) mass, which are ordinarily monitored in cities for air quality considerations, aerosol particle number size distributions, which are not rarely measured on-line continuously on longer run for research purposes and basic meteorological properties usually available were jointly evaluated. The largest changes occurred in the time interval of the severest limitations (partial lock-down in the Restriction phase from 28 March to 17 May 2020). Concentrations of NO, NO2, CO, total particle number (N6–1000) and particles with a diameter
<p>Characteristic phenomena in the Pannonian basin during the winter half year are the mist (500-1000 hours/year), the fog (150-300 hours/year) and the cold air pool with high air pollution concentrations. Formation, development and dissipation of fog events are complex processes that are impacted by short- and longwave radiation, condensation and evaporation, turbulent exchange, furthermore fog chemistry. The research presented here aims at exploring the interaction of these processes using field observations. To this end, complex field campaigns were conducted in Budapest (WMO code: 12843) and in the Si&#243; Valley, 6 km away from Si&#243;fok (12935) during 1 to 3-month periods in the last three winter half years.</p><p>Besides air chemistry and standard meteorological variables, the leaf wetness, surface and soil temperature, soil moisture, soil heat flux (Huskeflux), radiation budget components (CNR1) and turbulent fluxes based on eddy covariance (CSAT3, EC150) and gradient methods were measured above the grassland. Time resolutions of measurements for slow sensors were 10 sec or rather 1 minute and for eddy covariance system 10 Hz. The mist and fog periods were detected using a cloud camera (in Si&#243; Valley) and by synoptic observations in Budapest and Si&#243;fok.</p><p>Additional measurements in Budapest were i) the wind speed (<em>U</em>), air temperature (<em>T</em>) and relative humidity (<em>RH</em>) profiles together with Gill sonic anemometer at the top of a 30 m high tower, ii) LUFT CHM 15k ceilometer. SODAR and aviation meteorological measurements were also available from the<em> </em>Budapest Ferenc Liszt International Airport<em> (</em>LHBP<em>) </em>at 8 km distance.<em> </em>Other<em> </em>field experiments were done in the wet leeward Si&#243; Valley in 2018-19 and 2019-20. Vaisala WXT530 sensor, LUFT CHM 15k ceilometer, tethered balloon measurements with GRAW radiosondes and METEK SODAR measurements were also provided as additional information behind the energy budget measurements.</p><p>Our results confirmed that according to the expectations, we have recorded more foggy situations in the Si&#243; Valley than in Budapest (12843) and Si&#243;fok (12935). Radiation and advection type fog events were formed in most cases. The measured <em>RH</em> was above 95 and gradually increased during the onset period of fog. RH was around 100%, fluctuations could be measured less accurately. &#160;Dissipation of the fog is usually characterized by wind intensification and rise in the incoming solar radiation. The data of two field campaigns will be analyzed i) a cold pool situation in Si&#243; Valley in January 2020 and ii) the foggy season 2020-21 in Budapest. The developed complex (micrometeorological, furthermore air and liquid chemistry) database gives opportunity to validate numerical model results (WRF, CHIMERE and detailed box model) and to improve parameterizations of the numerical models.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.