Although several air pollution studies have examined the relationship between people living close to roadways and human health, we are unaware of studies that have examined the distance-decay of this effect based on a snapshot of congestion and focused on a micro-level traffic emission inventory. In this paper we estimate the distance-decay of premature mortality risk related to PM₂.₅ emitted by traffic congestion in Hamilton, Canada, in 2011 We employ the Stochastic User Equilibrium (SUE) traffic assignment algorithm to estimate congested travel times for each road link in our study area. Next, we used EPA's MOVES model to estimate mass of PM₂.₅, and then R-line dispersion model to predict concentration of PM₂.₅. Finally, we apply Integrated Exposure Response Function (IERF) to estimate PM₂.₅-related premature mortality at 100 m × 100 m grid resolution. We estimated total premature mortality over Hamilton to be 73.10 (95%CI: 39.05; 82.11) deaths per year. We observed that the proximity to a roadway increases the risk of premature mortality and the strength of this risk decreases as buffer sizes are increased. For example, we estimated that the premature mortality risk within buffer 0–100 m is 29.5% higher than for the buffer 101–200 m, 179.3% higher than for the buffer 201–300 m, and 566% higher than for the buffer 301–400 m. Our study provides a new perspective on exposure increments from traffic congestion. In particular, our findings show health effects gradients across neighborhoods, capturing microscale near-road exposure up to 2000 m of the roadway. Results from this research can be useful for policymakers to develop new strategies for the challenges of regulating transportation, land use, and air pollution.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Exposure to fine particulate matter (PM₂.₅) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM₂.₅ in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM₂.₅ concentrations in China, methods for generating reliable historical PM₂.₅ exposure, especially prior to the construction of PM₂.₅ monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM₂.₅ levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM₂.₅ measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM₂.₅ concentrations. The overall model CV R² and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013–2015) shown that this model can accurately predict historical PM₂.₅ concentrations at the monthly (R² = 0.74, RPE = 27.6%), seasonal (R² = 0.78, RPE = 21.2%) and annual (R² = 0.76, RPE = 16.9%) level. The annual mean predicted PM₂.₅ concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM₂.₅ concentrations for epidemiological studies on PM₂.₅ health effects in China.

Plant uptake factors (PUFs) are of great importance in human cadmium (Cd) exposure risk assessment while it has been often treated in a generic way. We collected 1077 pairs of vegetable-soil samples from production fields to characterize Cd PUFs and demonstrated their utility in assessing Cd exposure risks to consumers of locally grown vegetables. The Cd PUFs varied with plant species and pH and organic matter content of soils. Once normalized PUFs against soil parameters, the PUFs distributions were log-normal in nature. In this manner, the PUFs were represented by definable probability distributions instead of a deterministic figure. The Cd exposure risks were then assessed using the normalized PUF based on the Monte Carlo simulation algorithm. Factors affecting the extent of Cd exposures were isolated through sensitivity analyses. Normalized PUF would illustrate the outcomes for uncontaminated and slightly contaminated soils. Among the vegetables, lettuce was potentially hazardous for residents due to its high Cd accumulation but low Zn concentration. To protect 95% of the lettuce production from causing excessive Cd exposure risks, pH of soils needed to be 5.9 and above.

Air pollution forecasting is significant for public health and controlling pollution, and statistical methods are important air pollution forecasting techniques. Nevertheless, the research of AQI (air quality index) forecasting is very rare. So an accurate and stable AQI forecasting model is very urgent and necessary. For the high complex, volatile and nonlinear AQI series, this research presents a novel optimal-combined model based on CEEMD (complementary ensemble empirical mode decomposition), PSOGSA (particle swarm optimization and gravitational search algorithm), PSO (particle swarm optimization) and combined forecasting method. The proposed model effectively solves the blind combined forecasting. AQI series forecasts of five cities in North China show that the proposed model has the highest correct rate of forecasting classifications compared with the candidates. Totally, the presented model has the following advantages compared with the candidates: more robust forecasting performance, smaller forecasting error and better generalization ability.

Cs-137 is considered to be the most significant anthropogenic contributor to human dose and presents a particularly difficult remediation challenge after a dispersal following nuclear incident. The Chernobyl Nuclear Power Plant meltdown in April 1986 represents the largest nuclear accident in history and released over 80 PBq of 137Cs into the environment. As a result, much of the land in close proximity to Chernobyl, which includes the Polessie State Radioecology Reserve in Belarus, remains highly contaminated with 137Cs to such an extent they remain uninhabitable. Whilst there is a broad scale understanding of the depositional patterns within and beyond the exclusion zone, detailed mapping of the distribution is often limited. New developments in mobile gamma spectrometry provide the opportunity to map the fallout of 137Cs and begin to reconstruct the depositional environment and the long-term behaviour of 137Cs in the environment. Here, full gamma spectrum analysis using algorithms based on the peak-valley ratio derived from Monte Carlo simulations are used to estimate the total 137Cs deposition and its depth distribution in the soil. The results revealed a pattern of 137Cs distribution consistent with the deposition occurring at a time of flooding, which is validated by review of satellite imagery acquired at similar times of the year. The results were also consistent with systematic burial of the fallout 137Cs by annual flooding events. These results were validated by sediment cores collected along a transect across the flood plain. The true merit of the approach was confirmed by exposing new insights into the spatial distribution and long term fate of 137Cs across the floodplain. Such systematic patterns of behaviour are likely to be fundamental to the understanding of the radioecological behaviour of 137Cs whilst also providing a tracer for quantifying the ecological controls on sediment movement and deposition at a landscape scale.

Pacific Ocean trawl samples, stomach contents of laboratory-raised fish as well as fish from the subtropical gyres were analyzed by Raman micro-spectroscopy (RMS) to identify polymer residues and any detectable persistent organic pollutants (POP). The goal was to access specific molecular information at the individual particle level in order to identify polymer debris in the natural environment. The identification process was aided by a laboratory generated automated fluorescence removal algorithm. Pacific Ocean trawl samples of plastic debris associated with fish collection sites were analyzed to determine the types of polymers commonly present. Subsequently, stomach contents of fish from these locations were analyzed for ingested polymer debris. Extraction of polymer debris from fish stomach using KOH versus ultrapure water were evaluated to determine the optimal method of extraction. Pulsed ultrasonic extraction in ultrapure water was determined to be the method of choice for extraction with minimal chemical intrusion. The Pacific Ocean trawl samples yielded primarily polyethylene (PE) and polypropylene (PP) particles >1 mm, PE being the most prevalent type. Additional microplastic residues (1 mm - 10 μm) extracted by filtration, included a polystyrene (PS) particle in addition to PE and PP. Flame retardant, deca-BDE was tentatively identified on some of the PP trawl particles. Polymer residues were also extracted from the stomachs of Atlantic and Pacific Ocean fish. Two types of polymer related debris were identified in the Atlantic Ocean fish: (1) polymer fragments and (2) fragments with combined polymer and fatty acid signatures. In terms of polymer fragments, only PE and PP were detected in the fish stomachs from both locations. A variety of particles were extracted from oceanic fish as potential plastic pieces based on optical examination. However, subsequent RMS examination identified them as various non-plastic fragments, highlighting the importance of chemical analysis in distinguishing between polymer and non-polymer residues.

In this paper, results from receptor modelling performed on a well-characterised PM₁ dataset were combined to chemical light extinction data (bₑₓₜ) with the aim of assessing the impact of different PM₁ components and sources on light extinction and visibility at a European polluted urban area. It is noteworthy that, at the state of the art, there are still very few papers estimating the impact of different emission sources on light extinction as we present here, although being among the major environmental challenges at many polluted areas. Following the concept of the well-known IMPROVE algorithm, here a tailored site-specific approach (recently developed by our group) was applied to assess chemical light extinction due to PM₁ components and major sources.PM₁ samples collected separately during daytime and nighttime at the urban area of Milan (Italy) were chemically characterised for elements, major ions, elemental and organic carbon, and levoglucosan. Chemical light extinction was estimated and results showed that at the investigated urban site it is heavily impacted by ammonium nitrate and organic matter. Receptor modelling (i.e. Positive Matrix Factorization, EPA-PMF 5.0) was effective to obtain source apportionment; the most reliable solution was found with 7 factors which were tentatively assigned to nitrates, sulphates, wood burning, traffic, industry, fine dust, and a Pb-rich source. The apportionment of aerosol light extinction (bₑₓₜ,ₐₑᵣ) according to resolved sources showed that considering all samples together nitrate contributed at most (on average 41.6%), followed by sulphate, traffic, and wood burning accounting for 18.3%, 17.8% and 12.4%, respectively.

Few studies have estimated historical exposures to PM₁₀ at a national scale in China using satellite-based aerosol optical depth (AOD). Also, long-term trends have not been investigated.In this study, daily concentrations of PM₁₀ over China during the past 12 years were estimated with the most recent ground monitoring data, AOD, land use information, weather data and a machine learning approach.Daily measurements of PM₁₀ during 2014–2016 were collected from 1479 sites in China. Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data, land use information, and weather data were downloaded and merged. A random forests model (non-parametric machine learning algorithms) and two traditional regression models were developed and their predictive abilities were compared. The best model was applied to estimate daily concentrations of PM₁₀ across China during 2005–2016 at 0.1⁰ (≈10 km).Cross-validation showed our random forests model explained 78% of daily variability of PM₁₀ [root mean squared prediction error (RMSE) = 31.5 μg/m³]. When aggregated into monthly and annual averages, the models captured 82% (RMSE = 19.3 μg/m³) and 81% (RMSE = 14.4 μg/m³) of the variability. The random forests model showed much higher predictive ability and lower bias than the other two regression models. Based on the predictions of random forests model, around one-third of China experienced with PM₁₀ pollution exceeding Grade Ⅱ National Ambient Air Quality Standard (>70 μg/m³) in China during the past 12 years. The highest levels of estimated PM₁₀ were present in the Taklamakan Desert of Xinjiang and Beijing-Tianjin metropolitan region, while the lowest were observed in Tibet, Yunnan and Hainan. Overall, the PM₁₀ level in China peaked in 2006 and 2007, and declined since 2008.This is the first study to estimate historical PM₁₀ pollution using satellite-based AOD data in China with random forests model. The results can be applied to investigate the long-term health effects of PM₁₀ in China.

The concept of the smart city raised several technological and scientific issues including light pollution. There are various negative impacts of light pollution on economy, ecology, and heath. This paper deals with the census of the colour of light emitted by lamps used in a city environment. To this end, we derive a light bulb colour estimator based on Bayesian reasoning, directional data, and image formation model in which the usual concept of reflectance is not used. All choices we made are devoted to designing an algorithm which can be run almost in real-time. Experimental results show the effectiveness of the proposed approach.