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The negative effects of traffic, such as air quality problems and road congestion, put a strain on the infrastructure of cities and high-populated areas. A potential measure to reduce these negative effects are grocery home deliveries (e-grocery), which can bundle driving activities and, hence, result in decreased traffic and related emission outputs. Several studies have investigated the potential impact of e-grocery on traffic in various last-mile contexts. However, no holistic view on the sustainability of e-grocery across the entire supply chain has yet been proposed. Therefore, this paper presents an agent-based simulation to assess the impact of the e-grocery supply chain compared to the stationary one in terms of mileage and different emission outputs. The simulation shows that a high e-grocery utilization rate can aid in decreasing total driving distances by up to 255 % relative to the optimal value as well as CO 2 emissions by up to 50 %.
Background: Upsurge in cardiopulmonary dysfunctions in Enugu, Nigeria, involved mainly cement workers, automobile spray painters, woodworkers, and Cleaners and was worsened in the dry season, suggesting the need for an occupation-specific characterization of the disease features and seasonal evaluation of air quality for prevention and management.
Methods: We conducted a randomized cross-sectional study of eighty consenting participants (in Achara Layout, Enugu), comprising 20 cement workers (39.50 ± 14.95 years), 20 automobile spray painters (40.75 ± 9.85 years), 20 woodworkers (52.20 ± 9.77 years), and 20 cleaners (42.30 ± 9.06 years). The air quality, some haematological (fibrinogen-Fc, and C-reactive protein-CRP), and cardiopulmonary parameters were measured and analyzed using ANCOVA, at p < 0.05.
Results: The dry season particulate matter (PM) in ambient air exceeded the WHO standards in the New layout [PM10 = 541.17 ± 258.72 µg/m3; PM2.5 = 72.92 ± 25.81 µg/m3] and the University campus [PM10 = 244 ± 74.79 µg/m3; PM2.5 = 30.33 ± 16.10 µg/m3], but the former was twice higher. The PM differed significantly (p < 0.05) across the sites. Forced expiratory volume at the first second (FEV1) (F = 6.128; p = 0.001), and Peak expiratory flow rate (PEFR) (F = 5.523; p = 0.002), differed significantly across the groups. FEV1/FVC% was < 70% in cement workers (55.33%) and woodworkers (61.79%), unlike, automobile spray painters (72.22%) and cleaners (70.66%). FEV1 and work duration were significantly and negatively related in cement workers (r = -0.46; r2 = 0.2116; p = 0.041 one-tailed). CRP (normal range ≤ 3.0 mg/L) and Fc (normal range—1.5–3.0 g/L) varied in cement workers (3.32 ± 0.93 mg/L versus 3.01 ± 0.85 g/L), automobile spray painters (2.90 ± 1.19 mg/L versus 2.54 ± 0.99 mg/L), woodworkers (2.79 ± 1.10 mg/L versus 2.37 ± 0.92 g/L) and cleaners (3.06 ± 0.82 mg/L versus 2.54 ± 0.70 g/L).
Conclusion(s): Poor air quality was evident at the study sites, especially in the dry season. Cement workers and automobile spray painters showed significant risks of obstructive pulmonary diseases while woodworkers had restrictive lung diseases. Cement workers and cleaners recorded the highest risk of coronary heart disease (CRP ≥ 3.0 mg/L). The similarity in Fc and CRP trends suggests a role for the inflammation-sensitive proteins in the determination of cardiovascular risk in cement workers and cleaners. Therefore, there are occupation-specific disease endpoints of public health concern that likewise warrant specific preventive and management approaches among the workers.
Unter Crowdsensing versteht man Anwendungen, in denen Sensordaten kollaborativ von einer Menge von Freiwilligen erhoben werden. So kann Crowdsensing eingesetzt werden um die Luftqualität an Orten zu messen, an denen keine fest installierten Sensoren verfügbar sind. In Crowdsensing-Systemen müssen die Teilnehmer koordiniert und die Messdaten verarbeitet werden, um relevante Daten zu erhalten. Im Rahmen der Abschlussarbeit wurde ein System konzipiert und prototypisch umgesetzt, das auf einem Raspberry Pi (unter Einsatz geeigneter Sensoren) Sensordaten erhebt und mit der Complex Event Processing Technologie verarbeitet.