Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Evaluation and Prediction of PM10 and PM2.5 from Road Source Emissions in Kuala Lumpur City Centre

Particulate matter (PM) is one of the major pollutants emitted by vehicles that adversely affect human health and the environment. This study evaluates and predicts concentrations and dispersion patterns of PM10 and PM2.5 in Kuala Lumpur city centre. The OML-Highway model calculates hourly time series of PM10 and PM2.5 concentrations and distribution caused by traffic emissions under different scenarios; business as usual (BAU) and 30% traffic reduction to see the impact of traffic reduction for sustainable traffic management. Continuous PM10 and PM2.5 data from a nearby monitoring station were analysed for the year 2019 and compared with modelled concentrations. Annual average concentration at various locations of interest for PM10 and PM2.5 during BAU runs were in the ranges 41.4–65.9 µg/m3 and 30.4–43.7 µg/m3 respectively, compared to during the 30% traffic reduction run ranging at 40.5–59.5 µg/m3 and 29.9–40.3 µg/m3 respectively. The average concentration of PM10 and PM2.5 at the Continuous Air Quality Monitoring Station (CAQMS) was 36.4 µg/m3 and 28.2 µg/m3 respectively. Strong correlations were observed between the predicted and observed data for PM10 and PM2.5 in both scenarios (p < 0.05). This research demonstrated that the reduction of traffic volume in the city contributes to reducing the concentration of particulate matter pollution.

Effects of COVID-Induced Mobility Restrictions and Weather Conditions on Air Quality in Hungary

Similarly to other countries, the first wave of the COVID pandemic induced a collapse of mobility in Hungary during the spring of 2020. From the environmental perspective, the obtained road traffic reduction of 20–50% could be regarded as an undesired traffic regulation experiment. Air quality impacts within Hungary were evaluated based on data from 52 monitoring sites measuring concentrations of pollutants NOx, O3, and PM10. Air pollution during the lockdown was compared to the same period (February–June) in the reference years 2014–2019. The large spatial heterogeneity of the air quality response was explored. The emission reduction coincided with the extreme weather of 2020, characterized by unusually warm pre-lockdown February and spring drought. The anomalously low pre-lockdown air pollution was further reduced (NOx) or increased (PM10) during the restrictions. Compared to the previous years, NOx concentrations during the curfew were found to differ between −4.1 and +0.2 standard deviations (median −1.55 SD), or −45% and +3% (median −18%) among different monitoring locations. Ozone concentrations were unusually high due to both weather and chemical reasons (median +11% or +0.8 SD), while the PM10 response was modest and largely weather-driven (median +7% or +0.4 SD).

Changes in Urban Gas-Phase Persistent Organic Pollutants During the COVID-19 Lockdown in Barcelona

The composition of polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), hexachlorobenzene (HCB), pentachlorobenzene (PeCB), and organophosphate flame retardants (OPFRs) present in the gas-phase fraction of the atmosphere of Barcelona was analyzed during the SARS-CoV-2 coronavirus disease (COVID-19) lockdown and prior to this period. The changes in daily concentrations of CO, NO, NO2, O3 and particulate matter smaller than 10 μm (PM10) were considered for comparison. Bayesian analysis considering serial dependencies and seasonality showed statistically significant decreases of CO, NO, NO2, and PM10 (between −28 and −76%) and O3 increases (+45%) during lockdown. However, the lockdown concentration decreases of PeCB (−90.5%, from 8.5 to 0.8 pg m–3), HCB (−79%, 25.5–5.4 pg m–3) and some PAHs, such as benz[a]anthracene (−87%, 120–17 pg m–3) and pyrene (−81%, 3,500–680 pg m–3), were even stronger. The PAH depletion ranged between −68 and −87% that could be primarily associated with the strong reduction of traffic mobility during this period (−80%). Besides traffic reduction, the observed air quality improvements could be related to lower generation of solid urban residues (−25%) and the subsequent decrease of urban waste incineration (between −25 and −28%). Tributyl phosphate also showed a reduction in concentration during lockdown but the other OPFRs were seemingly not affected by this restriction, possibly as a result of the uniform release from the emission sources, e.g., construction material, industrial applications, and household products.

Changes of cirrus cloud properties and occurrence over Europe during the COVID-19 caused air traffic reduction

By inducing linear contrails and contrail cirrus, air traffic has a main impact on the ice cloud coverage and occurrence. During the COVID-19 pandemic the civil air traffic over Europe was significantly reduced: in March and April 2020 to about 80 % compared to the year before. This unique situation allows to study the effect of air traffic on cirrus clouds. This work investigates based on satellite lidar measurements if and how cirrus cloud properties and occurrence changed over Europe in the course of COVID-19. Cirrus cloud properties are analyzed for different years, which showed similar meteorological conditions for March and April as they were found for 2020. Comparing these years shows that the cirrus cloud occurrence was reduced by about 30 % with smaller cloud thicknesses found in April 2020. The average thickness of cirrus clouds was reduced to 1.18 km in April 2020 compared to a value of 1.40 km under normal conditions. In addition, the cirrus clouds measured in April 2020 possess smaller mean values of the particle linear depolarization ratio (PLDR) than the previous years at high significance level, especially at colder temperatures (T

Climate impact of aircraft-induced cirrus assessed from satellite observations before and during COVID-19

&lt;p&gt;Aircraft produce contrail in suitable atmospheric conditions, and these may spread out into cirrus. However, it is unclear how large this effect and its implied radiative forcing is. Here we use the opportunity of the COVID-19 related aircraft traffic reduction in boreal spring 2020 in comparison to the traffic in 2019 to assess satellite data. MODIS retrievals are examined for 2020 vs. the climatology 2011 to 2019. In order to account for weather variability, circulation analogues are defined for each region and day of the Spring 2020 period, and the cirrus coverage and emissivity in springtimes 2011 - 2019 is assessed for comparison to 2020. In conclusion, we find that cirrus are reduced by 9&amp;#177;1.5% in absolute terms. This is consistent with a trend analysis. The implied radiative forcing by aviation-induced cirrus is assessed at 49&amp;#177;28 Wm-2.&amp;#160;&lt;/p&gt;