In this study, we demonstrate that regression analysis of trajectories residence time estimates the contributions of geographical sectors to fine and coarse particle mass in urban receptor sites. We applied the methodology to coarse and fine particles in Amsterdam, Athens, Birmingham and Helsinki. The sectors with the highest contributions on PM2.5 and PM10–2.5 for Amsterdam and Birmingham were Central/Eastern Europe and the Atlantic Ocean/North Sea, respectively. For Athens, the four sectors within 500 km accounted for the largest fraction of PM2.5. The Mediterranean Sea and North Africa added more than half of PM10–2.5 in Athens. For Helsinki, more than 50% of PM2.5 and PM10–2.5 were from sources outside Finland. This approach may be applied to assess the impact of transport on particle mass levels, identify the spatial patterns of particle sources and generate valuable data to design national and transnational efficient emission control strategies.

Many citizens live, work, commute, or visit traffic intensive spaces and are exposed to high levels of chemical health stressors. However, urban conurbations worldwide present monitoring “shortage” – due to economical and/or practical constraints – for toxic stressors such as xylene isomers, which can pose human health risks. This “shortage” may be covered by the establishment of associations between rarely monitored substances such as xylenes and more frequently monitored (i.e. benzene) or usually monitored (i.e. CO). Regression analysis is used and strong statistical relationships are detected. The adopted models are applied to EU cities and comparison between measurements and predictions depicts their representativeness. The analysis provides transferability insights in an effort to bridge the gap between traffic-related stressors. Strong associations between substances of the air pollution mixture may be influential to interpret the complexity of the causal chain, especially if a synergetic exposure assessment in traffic intensive spaces is considered.

This study investigated associations between long-term exposure to ambient volatile organic compounds (VOCs) and mortality. 58,760 Toronto residents (≥35 years of age) were selected from tax filings and followed from 1982 to 2004. Death information was extracted using record linkage to national mortality data. Land-use regression surfaces for benzene, n-hexane, and total hydrocarbons were generated from sampling campaigns in 2002 and 2004 and assigned to residential addresses in 1982. Cox regression was used to estimate relationships between each VOC and non-accidental, cardiovascular, and cancer mortality. Positive associations were observed for each VOC. In multi-pollutant models the benzene and total hydrocarbon signals were strongest for cancer. The hazard ratio for cancer that corresponded to an increase in the interquartile range of benzene (0.13 μg/m3) was 1.06 (95% CI = 1.02–1.11). Our findings suggest ambient concentrations of VOCs were associated with cancer mortality, and that these exposures did not confound our previously reported associations between NO2 and cardiovascular mortality.

Diurnal temperature range (DTR) is an important meteorological indicator associated with global climate change, but little is known about the effects of DTR on mortality. We examined the effects of DTR on cause-/age-/education-specific mortality in Guangzhou, a subtropical city in China during 2003–2010. A quasi-Poisson regression model combined with distributed lag non-linear model was used to examine the effects of DTR, after controlling for daily mean temperature, air pollutants, season and day of the week. A 1 °C increase in DTR at lag 0–4 days was associated with a 0.47% (95% confidence interval: 0.01%–0.93%) increase in non-accidental mortality. Stroke mortality was most sensitive to DTR. Female, the elderly and those with low education were more susceptible to DTR than male, the youth and those with high education, respectively. Our findings suggest that vulnerable subpopulations should pay more attention to protect themselves from unstable daily weather.

Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RF with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information.

Ambient visibility is a complex manifestation arising out of interactions among many atmospheric variables, including ambient aerosol load, and region specific geophysical characteristics. To functionally relate visibility impairment in Delhi region during winter months-months marred with poor visibility conditions–a novel experiment was designed to relate visibility with ambient aerosol load (PM2.5), and relevant meteorological variables: dew point temperature (Dp), height of planetary boundary layer (PBL), ambient temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WD). Time series data sets of Visibility(t) and other variables were subjected to non–linear decomposition using Empirical Mode Decomposition Method (EMD), enabling to obtain total cyclic and acyclic–trend components embedded in all data–sets. Extracted total cyclic visibility components were functionally related with the corresponding components associated with PM2.5 load and meteorological variables. Decomposed acyclic–trend component of the visibility, representing time dependent acyclic trend (AT), was separately related with the corresponding AT components of the considered meteorological variables. The decomposed components of the visibility (total cyclic and AT) were subjected to multiple linear regression to establish a functional relationship between them and a set of variables among the considered variables. The analysis suggests that acyclic–trend associated with Visibility(t) can be predicted better as opposed to the Visibility(t)cyclic component.

Satellite remote sensing technology has a great potential to measure the properties of atmosphere and has shown marked advancements through the last decade, in monitoring nitrogen oxide species (NOX) in troposphere. NO2 in troposphere is one of the key factors which determine the air quality and has serious implications on human health and plant growth. It is also well known for its indirect contribution to climate change. The identification of spatial and temporal variations of NO2 is necessary for any effective mitigation plan to curb its obnoxious abundance. Tropospheric NO2 measurements provided by the satellite remote sensor “SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY” (SCIAMACHY) are utilized here to identify the regions across India where a concentration of NO2 exceeds the permissible healthy level. A new approach based on empirical approximation is attempted to normalize the unit of satellite measurement with the unit of existing air quality standard. Trend analysis for all the regions are carried out by means of a non–linear regression method. The geospatial and statistical analysis of monthly tropospheric NO2 data from the full operational period of SCIAMACHY (2002 August–2012 March) have resulted in the identification of 12 hot spot regions across India among which most of them exhibited a significant increasing trend. Some of the rural districts which were not previously implicated for NO2 pollution risk are also recognized here. This study illustrates the possibility of the use of satellite measurements in air quality monitoring and management in regional spatial scale. The effects of seasonal climatic changes in India on the ambient NO2 pollution level are also explained.

Air pollution control strategies have helped to improve the air quality over Europe over the last twenty years. Despite this success, the improvements have been insufficient to protect health of those who spend most of their time within urban areas and particularly near major roads. Given the inherent complexity of urban environments and the incomplete understanding of the physical and chemical processes involved in pollutant dispersion, it is a challenging task to estimate urban air quality. In order to address this issue, a new regression model for estimating the urban increment for all cities of a region up to Europe as a whole is developed. The model is able to capture the higher pollutant concentrations commonly found within urban areas for assessing the localized effects associated to urban emissions. This approach is used for estimating annual concentrations of PM10 and NO2 for all urban areas with more than 50 000 inhabitants in Germany for the reference year 2005. The results showed that there are differences on air quality levels across urban areas in Germany. This information is relevant when evaluating the impact of emission reduction policies on air quality, which should take into account the cost–benefit of each measure. Furthermore, as the modelling approach allows for flexibility changing initial conditions and building scenarios, the added values of this approach are the large spatial domain covered, the high spatial resolution, and its inherent flexibility to address other environmental issues, such as analysis of emission reduction scenarios, the human exposure to certain pollutants and their associated human health impacts.

Between November 1989 and March 2013, 1033 Antarctic fur seals Arctocephalus gazella were observed entangled in marine debris at Bird Island, South Georgia. The majority of entanglements involved plastic packaging bands (43%), synthetic line (25%) or fishing net (17%). Juvenile male seals were the most commonly entangled (44%). A piecewise regression analysis showed that a single breakpoint at 1994 gave the best description of inter-annual variability in the data, with higher levels of entanglements prior to 1994 (mean=110±28) followed by persistent lower levels (mean=28±4). Records of entanglements from other sites monitored in the Scotia Sea are also presented. Legislation imposed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) has, to a certain extent, been effective, but persistent low levels of seal entanglements are still a cause for concern at South Georgia.

PM10 and PM2.5 (particles with diameter less than 10 μm and 2.5 μm, respectively) aerosol samples were collected from 2002 to 2004 in Athens, Greece and analyzed for elements ranging from Na to Pb using X–ray fluorescence. Positive matrix factorization (PMF) was applied to identify and quantify the types of PM10 and PM2.5 sources. The agreement between calculated and measured particle mass concentrations was very good for both aerosol fractions. Mineral soil, road dust and sea salt particles were the most significant types of coarse particles (PM10–2.5) while their contributions to PM2.5 fraction were minimal. Secondary sulfate, diesel particles from shipping and other oil combustion activities and primary traffic exhausts were determined as the most important types of PM2.5 sources. The analysis of normalized average air mass residence times showed weak seasonal patterns on the pathways of air masses prior to their arrival in Athens. Using trajectory regression analysis, the four adjacent regions (less than 500 km) accounted for the largest fraction of fine sulfate and diesel particles. On the contrary, shipping emissions and neutralization by sea salt may explain the high contributions of the regions covering the Mediterranean Sea and Dardanelle straights. The four adjacent regions and central Europe accounted for most of road and mineral dust particles. The transport from northern Africa over Mediterranean Sea also appeared to influence the mineral dust particles.