| Literature DB >> 27539238 |
Dorie E Apollonio1, Nicole Wolfe2, Lisa A Bero3.
Abstract
BACKGROUND: Exposure to pollution is a significant risk to human health. However few studies have attempted to identify the types of policy interventions that can reduce the health risks of pollution exposure in the United States. The study objective was to conduct a realist review of policy interventions conducted or aimed at reducing chemical exposures in humans or the environment where exposure was measured.Entities:
Keywords: Environmental toxic substances; Health policy; Pollution; Systematic review
Mesh:
Substances:
Year: 2016 PMID: 27539238 PMCID: PMC4991102 DOI: 10.1186/s12889-016-3461-7
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Fig. 1Sample context-mechanism-outcome configuration for an economic study of environmental purchasing [15]
Study characteristics by type of intervention
| Intervention type | Exposure type | Study | Outcome measures | Measurement type | Study design | Study site |
|---|---|---|---|---|---|---|
| Regulatory ( | airborne pollutants | Bahadur 2010 | black carbon | environmental | interrupted time series | California |
| Gego 2007 | ozone | environmental | interrupted time series | Eastern US | ||
| Aleksic 2013 | ozone | environmental | interrupted time series | New York | ||
| Cheung 2005 | particulate matter | environmental | interrupted time series | Los Angeles Basin, CA | ||
| Thomas 2013 | sulfur | environmental | cross-sectional | Potomac River, WV | ||
| Dallman 2011 | multiple pollutants | environmental | interrupted time series | Oakland, CA | ||
| Bishop 2013 | multiple pollutants | environmental | interrupted time series | South Coast Air Basin, CA | ||
| Davis 2012 | multiple pollutants | environmental | time series cross-sectional | California | ||
| Pokharel 2013 | multiple pollutants | environmental | time series cross-sectional | multi-city data set (USA) | ||
| Lin 2013 | respiratory diagnosis | human | time series cross-sectional | New York | ||
| Mott 2002 | carbon monoxide related deaths | human | interrupted time series | national data set (USA) | ||
| lead paint | Brown 2001 | blood levels & hard surfaces | human, environmental | retrospective cohort | 2 northeastern states (USA) | |
| Galke 2001 | blood levels & hard surfaces | human, environmental | interrupted time series | multi-city data set (USA) | ||
| Rich 2002 | hard surfaces tested | environmental | randomized controlled trial | New Jersey | ||
| Breysse 2007 | hard surfaces tested | environmental | interrupted time series | Baltimore, MD | ||
| water pollution | Lakind 2010 | trihalomethane (THM) levels | human | time series cross-sectional | national data set (USA) | |
| Dorsey 2010 | fecal indicator bacteria | environmental | interrupted time series | California beaches | ||
| Hundal 2014 | trace metal concentrations | environmental | interrupted time series | multi-city data set (USA) | ||
| Daberkow 2001 | nitrate-nitrogen levels | environmental | time series cross-sectional | Central Platte Valley, NE | ||
| Kauffman 2011 | multiple pollutants | environmental | interrupted time series | Delaware Basin, DE | ||
| pesticides | Clune 2012 | pesticide levels | human | interrupted time series | national data set (USA) | |
| Educational ( | lead paint | Aschengrau 1998 | blood levels & hard surfaces | human, environmental | non-randomized controlled trial | Boston, MA |
| water pollution | Postma 2011 | well water contaminants | environmental | cross-sectional study | Montana & Washington state | |
| Economic ( | airborne pollutants | Lu 2012 | sulfur dioxide emissions | environmental | time series cross-sectional | national data set (USA) |
| hazardous waste | Eagan 2002 | mercury levels | environmental | interrupted time series | Great Lakes region |
Fig. 2Study flow diagram
Data extraction tool and example of data extracted for a regulatory study of truck emissions [16]
| Category of intervention | Regulatory |
|---|---|
| Regulatory policy | airborne pollutants |
| Year | 2011 |
| Author | Dallmann, T. |
| Title | Effects of Diesel Particle Filter Retrofits and Accelerated Fleet Turnover on Drayage Truck Emissions at the Port of Oakland |
| Journal | Environmental Science & Technology |
| Location | Oakland, CA |
| Study design | Interrupted time series |
| Study aim | Measure emissions for drayage trucks operating at the Port of Oakland before and after the implementation of diesel particle filter retrofits and truck replacements. |
| Inclusion criteria | Diesel trucks driving to the Port of Oakland on 7th Street |
| Exclusion criteria | Trucks entering from other points |
| Population | 3550 trucks passing the field sampling site on four dates in 2009–2010 during sampling periods |
| Sample size (treatment/control) | 3550 trucks, ~70 % estimated to be drayage trucks |
| Exposure | Air sampling line extended above vertical exhaust stacks of trucks driving below overpass sampling site |
| Intervention | Diesel particlate filter retrofits and accelerated truck replacement (no breakdown) |
| Duration of intervention | 4 weekday sampling periods ranging from 2.75 to 6.5 h over 7 months (Nov 2009-Jun 2010) |
| Comparison | Pre- and post-implementation of the California Air Resources Board drayage truck emission control regulation (2010) |
| Outcomes measures | levels of carbon dioxide, nitrogen dioxide, black carbon, particulate matter |
| Outcome verification (if self-reported) | air quality samples drawn to gas and particulate phase analyzers |
| Effect modifiers measured | n/a |
| Results | CO2: not reported; NO: decrease of 40 %; BC: reduction of 50 %; PM: 5x increase in trucks with no measurable PM, emissions data not reported due to poor instrument response; summary: reductions in BC caused by retrofit/replacement, reductions in NO caused by replacement |
| Risk of bias | choice of observation dates, concealment of allocation, blinding: not described; incomplete outcome data: emissions for CO2 and PM not reported |
| Funding source | Bay Area Air Quality Management District, University of California research program in sustainable transportation |
| Additional information |