In the framework of the multicenter ESCAPE (European Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health impacts–Integrated Methodologies for Assessing Particulate Matter) projects, we added standardized exposure assessment for air pollution to mortality data from 19 ongoing cohort studies across Europe. Associations of particle mass (PM2.5, PM10, PMcoarse, and PM2.5 absorbance) and nitrogen oxides (NO2 and NOx) with natural-cause mortality in the same cohorts have been reported previously (). We found a statistically significant elevated hazard ratio for PM2.5 of 1.07 [95% confidence interval (CI): 1.02, 1.13] per 5 μg/m3. In this paper we report associations with particle elemental composition in 19 European cohorts to assess whether specific components are associated with natural-cause mortality. A second aim was to assess whether the previously reported association with PM2.5 mass was explained by specific elements. Associations of particle composition and cardiovascular mortality have been published separately ().
For example, an experiment like this could provide insight into the effects of increasing or decreasing CO2 and its effect on water temperature…
Find or create two empty and identical glass-covered greenhouses (or other transparent buildings) that are located side-by-side, with neither structure affected differently by trees or shade or other outdoor environmental conditions. Inside each building place matching containers (large, preferably) filled with the same amount of water in each. Measure the baseline conditions of water temperature with precise thermometers, and measure the baseline CO2 concentration each building — which will presumably be the same or similar in each. In the first building (control), do not alter the internal natural CO2 concentration, but leave it at baseline (somewhere close to 400 ppm). In the second building, inject incrementally increasing quantities of CO2 (e.g., 500 ppm, 1,000 ppm, 1,500 ppm) with a CO2 generator (which are used in greenhouses to stimulate plant growth). Use a CO2 monitor (also used routinely in greenhouses) to measure and control the amount of CO2 contained in the experimental building. After a specified time lapse, measure the water temperature change, if any, for both the control building and the building with added CO2 from identical depths and locations for each container. Finally, reverse the process and incrementally draw down the CO2 injection in the experimental building while again gauging water temperature changes for each building.
Because your way of comparing both is “new”. Nobody would actually do something like that, particularly because the amount of back radiation also depends on surface temperatures. You compared seasonal data and seem to be inferring that this means that both effects work against each other. Try comparing year over year data and what effects increases in both water vapor concentration and CO2 concentration have year over year. Then we are talking.
Associations with other elements. None of the other elements evaluated in our analysis were significantly associated with mortality, though HRs were positive for almost all elements. There was greater heterogeneity among individual cohort effect estimates for elements other than PM2.5 S, though for most elements the heterogeneity was not statistically significant. There was little evidence of associations with Cu and Fe, which were mainly selected as markers of (non-tailpipe) traffic emissions. Source apportionment studies conducted elsewhere have reported that Fe is associated mostly with road dust and brake abrasion, whereas Cu is associated with tire and brake abrasion (reviewed by ). Our land use regression models had the best fit for these elements because traffic predictors were available and traffic sites were overrepresented in the measurement campaign. Therefore, we believe that the lack of an association in our study is unlikely to be attributable to exposure measurement error. In our previous analysis of the same set of cohorts, we estimated nonsignificant positive HRs for NO2 (1.01; 95% CI: 0.99, 1.03 per 10 μg/m3), NOx (1.02; 95% CI: 1.00, 1.04 per 20 μg/m3), and PM2.5 aborbance (1.02; 95% CI: 0.97, 1.07 per 10–5/m), pollutants affected by tailpipe emissions ().
Interpretation of S associations. Toxicological studies have provided little support for a causal effect of sulfate, despite fairly consistent associations in epidemiological studies (). Sulfate may indirectly affect health, for example, by solubilizing metals and thereby increasing their bioavailability, and by catalyzing the formation of secondary organic PM (). We identified associations with small-scale spatial variations in S and we speculate that this may reflect an influence of primary combustion from S-containing fuels and serve as a marker of within-city air pollution differences, that is, between city centers and surrounding areas.
We tested whether effect estimates differed for cohorts for which the land use regression model cross-validation explained variance was smaller or larger than 50% by computing the chi-square test of heterogeneity. In addition, we tested whether effect estimates differed by region of Europe (North: Sweden, Norway, Finland, Denmark; West and Middle: United Kingdom, the Netherlands, Germany, France, Austria, and Switzerland; South: Italy and Greece). We did not perform effect modification analyses for individual-level variables because this paper focuses on differences in effect estimates related to elemental composition. Only sex was an effect modifier for the association between PM2.5 and natural mortality in the same cohorts ().
Two-pollutant models were conducted for each element by adjusting for particle mass (PM2.5, PM10, PMcoarse), PM2.5 absorbance, NO2, NOx, and other elements in separate models. Because two pollutants may reflect the same source, two-pollutant models representing the independent effect of two pollutants may be difficult to interpret. Therefore, each two-pollutant model was restricted to data from studies for which the correlation between the two pollutants was ≤ 0.7.
Man the trolls here try and debunk this – but here is there reasoning – BUT c02 causes back radiation – of course he considered this. It’s as if they don’t even read the paper – I guess it goes against the cannon of the AGW faith, therefore is heretical and must be wrong – of COURSE water vapor cancels out any impact by .02% of our atmosphere – it’s you IPCC grovelers that recognize this and try to turn it into a positive feedback loop to get your runaway effect – in fact without considering water vapor, AGW theory as defined by the IPCC and MIKE hockey stick MANN would not be trying to say that man’s 3.75% addition to natural c02 will kill us all!!! Give your credentials and a clear refutation, or you are simply a troll
In sensitivity analyses, we added prevalent hypertension and physical activity to model 3, and additionally adjusted for the classical cardiovascular risk factors prevalent diabetes and cholesterol level. Extended confounder models were used in sensitivity analyses because some potential effects of air pollution might be mediated (e.g., hypertension) or affected (e.g, physical activity) by these factors.
It is obvious from your “reply” (“When you learn how the greenhouse effect works”) that you cannot answer the question. The GISS article you linked in your other post is laced with one assumption after another presupposing that CO2 is the primary control knob of temperature and making the utterly ridiculous claim that acts like a thermostat in a house. If that were true, then why does the most comprehensive temperature record (The US historical data set) show that at weather stations that have been in operation for over 100 years show absolutely no statistically significant change in temperature during the same months of the year? I downloaded the data from all 13 weather stations here in Pennsylvania and there simply is no trend in any month over more than 120 years despite the enormous increase in percentage of atmospheric CO2. If CO2 is the temperature control knob, then why does temperature drop 30-40 degrees overnight in dry, arid conditions vs. only 5-10 degrees in humid conditions? Because as experiments show, condensation in the humid environment releases heat mitigating the temperature drop – in other words water vapor. In science, the ultimate arbitrator for answering questions is experiment rather than theoretical arguments especially the failed greenhouse gas theory.
As described earlier, the association between natural-cause mortality and particle components was analyzed in each cohort separately, following the analysis protocol of the ESCAPE study (). A common STATA script (StataCorp, College Station, TX, USA) was used which was explained in a training workshop for all local analysts. Cohort-specific results were sent to the coordinating institute [the Institute for Risk Assessment Sciences (IRAS), Utrecht University] for central evaluation. Cohort-specific effect estimates were combined by random-effects meta-analysis. Pooling of the cohort data was not possible due to data transfer and privacy issues.