A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation

Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers

On the convergence of double integrals and a generalized version of Fubini's theorem on successive integration

Let the function f: \bar{\R}^2_+ \to \C be such that f\in L^1_{\loc} (\bar{\R}^2_+). We investigate the convergence behavior of the double integral \int^A_0 \int^B_0 f(u,v) du dv \quad {\rm as} \quad A,B \to \infty,\leqno(*) where $A$ and $B$ tend to infinity independently of one another; while using two notions of convergence: that in Pringsheim's sense and that in the regular sense. Our main result is the following Theorem 3: If the double integral (*) converges in the regular sense, or briefly: converges regularly, then the finite limits $$\lim_{y\to \infty} \int^A_0 \Big(\int^y_0 f(u,v) dv\Big) du =: I_1 (A)$$ and $$\lim_{x\to \infty} \int^B_0 \Big(\int^x_0 f(u,v) du) dv = : I_2 (B)$$ exist uniformly in $0<A, B <\infty$, respectively; and $$\lim_{A\to \infty} I_1(A) = \lim_{B\to \infty} I_2 (B) = \lim_{A, B \to \infty} \int^A_0 \int^B_0 f(u,v) du dv.$$ This can be considered as a generalized version of Fubini's theorem on successive integration when f\in L^1_{\loc} (\bar{\R}^2_+), but $f\not\in L^1 (\bar{\R}^2_+)$

How much do needlestick injuries cost? a systematic review of the economic evaluations of needlestick and sharps injuries among healthcare personnel

objective. To provide an overview of the economic aspects of needlestick and sharps injury (NSI) management among healthcare personnel (HCP) within a Health Technology Assessment project to evaluate the impact of safety-engineered devices on health care methods. A systematic review of economic analyses related to NSIs was performed in accordance with the PRISMA statement and by searching PubMed and Scopus databases (January 1997–February 2015). Mean costs were stratified by study approach (modeling or data driven) and type of cost (direct or indirect). Costs were evaluated using the CDC operative definition and converted to 2015 International US dollars (Int$). results. A total of 14 studies were retrieved: 8 data-driven studies and 6 modeling studies. Among them, 11 studies provided direct and indirect costs and 3 studies provided only direct costs. The median of the means for aggregate (direct + indirect) costs was Int$747 (range, Int$199–Int$1,691). The medians of the means for disaggregated costs were Int$425 (range, Int$48–Int$1,516) for direct costs (9 studies) and Int$322 (range, Int$152–Int$413) for indirect costs (6 studies). When compared with data-driven studies, modeling studies had higher disaggregated and aggregated costs, but data-driven studies showed greater variability. Indirect costs were consistent between studies, mostly referring to lost productivity, while direct costs varied widely within and between studies according to source infectivity, HCP susceptibility, and post-exposure diagnostic and prophylactic protocols. Costs of treating infections were not included, and intangible costs could equal those associated with NSI medical evaluations. conclusions. NSIs generate significant direct, indirect, potential, and intangible costs, possibly increasing over time. Economic efforts directed at preventing occupational exposures and infections, including provision of safety-engineered devices, may be offset by the savings from a lower incidence of NSIs