Purpose: – Proactive planning strategies for “slow-onset” disruptions that affect humanitarian supply chains (SC) developed to address chronic pressing societal problems, can have a significant impact on boosting the operational and financial performance of these chains. The purpose of this paper is to develop a methodology that quantifies the impact of a risk mitigation strategy widely employed in commercial SCs, namely emergency sourcing (ES), on the performance of humanitarian SCs taking into account backorders’ clearance time, unsatisfied demand, and cost. Design/methodology/approach: – Discrete event simulation is employed in order to evaluate alternative ES strategies based on a total cost criterion, which incorporates inventory-related costs, as well as premium contract costs paid for emergency replenishment. Backorders’ clearance time and time-to-recovery are also employed as a design parameters. Findings: – The results document the significant impact of disruptions on expected total cost, and the beneficial role of ES in hedging against disruptions. To that end, the proposed methodology determines the optimal emergency contracted capacity for a given premium, or alternatively the maximum premium cost value that ensures the feasibility of the implemented ES strategy in the long-run, along with the associated cost and time savings, and reduction of the unsatisfied demand. Originality/value: – The fundamental objective is to provide a decision-making support methodology for deciding on whether to implement an ES strategy or not in humanitarian SCs, and the level of the optimal contracted reserved capacity. The results could be of great value to aid providers, policy-makers, and regulators.

This study investigates pollutant emissions and fuel consumption of six Euro VI hybrid-diesel public transport buses operating on different scheduled routes in a metropolitan urban road network. Portable emission measurement systems (PEMS) are used in measurements and results are compared to those obtained from a paired number of Euro V conventional buses of the same body type used as control over the same routes. The selected routes vary from urban to highway driving and the experimentation was conducted over the first half of 2015. The available emissions data correspond to a wide range of driving, operating, and ambient conditions. Fuel consumption, distance- and energy-based emission levels are derived and presented in a comparative manner. The effect of different factors, including speed, ambient temperature, and road grade on fuel consumption and emissions performance is investigated. Mean fuel consumption of hybrid buses was found 6.1% lower than conventional ones, from 20% lower up to 16% higher, over six routes tested in total. The mean route difference between the two technologies was not statistically significant. Air conditioning decreased consumption benefits of the hybrid buses. Decrease of the mean route speed from 15 km h⁻¹ tο 8 km h⁻¹ increased the hybrid buses consumption by 63%. Nitrogen oxides (NO_x) emissions of the Euro VI hybrid buses were 93 ± 5% lower than conventional Euro V ones. Nitrous oxide (N₂O) emissions from hybrid Euro VI buses made up 5.9% of total greenhouse gas emissions and largely offset carbon dioxide (CO₂) benefits. The results suggest that hybrid urban buses need to be assessed under realistic operation and environmental conditions to assess their true environmental and fuel consumption benefits.

Mobile monitoring devices equipped with low-cost gas sensors in fixed stations are an emerging solution to enhance the spatial coverage of air quality monitoring networks. We estimated the measurement accuracy of two AQMesh devices, evaluated their agreement, and examined the related calibration characteristics. Three widely used calibration approaches were investigated, namely uni- and multi-variate linear regression analysis and the random forest algorithm. Two identical commercial AQMesh platforms (monitoring NO, NO2, O3, and SO2) were installed on a fixed municipal station for 4 consecutive weeks. Widely used statistical indexes were employed to evaluate device performance and calibration outcomes. The devices exhibited favorable performance in following the pattern of the station’s reference time series in a 10-min average resolution. Nevertheless, their performance was lower, with respect to the reference values, in terms of the average error and overall bias. The calibration improved the agreement between the device and reference measurements. The emission time series of each device was consistent with the other (pre- and post-calibration) in terms of measurement patterns and point-by-point deviations. The three alternative methodologies had similar calibration performance overall. The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.