Smoke emitted from Indonesian peatland fires has caused dense haze and serious air pollution in Southeast Asia such as visibility impairment and adverse health impacts. To mitigate the Indonesian ...peatland fire aerosol impacts, an effective strategy and international framework based on the latest scientific knowledge needs to be established. Although several attempts have been made, limited data exist regarding the chemical characteristics of peatland fire smoke for the source apportionment. In order to identify the key organic compounds of peatland fire aerosols, we conducted intensive field studies based on ground-based and source-dominated sampling of PM2.5 in Riau Province, Sumatra, Indonesia, during the peatland fire seasons in 2012. Levoglucosan was the most abundant compound among the quantified organic compounds at 8.98 ± 2.28% of the PM2.5 mass, followed by palmitic acid at 0.782 ± 0.163% and mannosan at 0.607 ± 0.0861%. Potassium ion was not appropriate for an indicator of Indonesian peatland fires due to extremely low concentrations associated with smoldering fire at low temperatures. The vanillic/syringic acids ratio was 1.06 ± 0.155 in this study and this may be a useful signature profile for peatland fire emissions. Particulate n-alkanes also have potential for markers to identify impact of Indonesian peatland fire source at a receptor site.
•Organic compounds in PM2.5 emitted from peatland fires were characterized.•Potassium cannot be used as a source indicator for peatland fires.•We found some potential source indicators of peatland fire.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Although increasing hidden layers can improve the ability of a neural network in modeling complex nonlinear relationships, deep layers may result in degradation of accuracy due to the problem of ...vanishing gradient. Accuracy degradation limits the applications of deep neural networks to predict continuous variables with a small sample size and/or weak or little invariance to translations. Inspired by residual convolutional neural network in computer vision, we developed an encoder-decoder full residual deep network to robustly regress and predict complex spatiotemporal variables. We embedded full shortcuts from each encoding layer to its corresponding decoding layer in a systematic encoder-decoder architecture for efficient residual mapping and error signal propagation. We demonstrated, theoretically and experimentally, that the proposed network structure with full residual connections can successfully boost the backpropagation of signals and improve learning outcomes. This novel method has been extensively evaluated and compared with four commonly used methods (i.e., plain neural network, cascaded residual autoencoder, generalized additive model, and XGBoost) across different testing cases for continuous variable predictions. For model evaluation, we focused on spatiotemporal imputation of satellite aerosol optical depth with massive nonrandomness missingness and spatiotemporal estimation of atmospheric fine particulate matter <inline-formula> <tex-math notation="LaTeX">\leq 2.5~\mu \text{m} </tex-math></inline-formula> (PM 2.5 ). Compared with the other approaches, our method achieved the state-of-the-art accuracy, had less bias in predicting extreme values, and generated more realistic spatial surfaces. This encoder-decoder full residual deep network can be an efficient and powerful tool in a variety of applications that involve complex nonlinear relationships of continuous variables, varying sample sizes, and spatiotemporal data with weak or little invariance to translation.
European winter wheat cultivar “Tabasco” was reported to have resistance to powdery mildew disease caused by
Blumeria graminis
f. sp.
tritici
(
Bgt
) in China. In previous studies, Tabasco was ...reported to have the resistance gene designated as
Pm48
on the short arm of chromosome 5D when a mapping population was phenotyped with pathogen isolate
Bgt19
collected in China and was genotyped with simple sequence repeat (SSR) markers. In this study, single-nucleotide polymorphism (SNP) chips were used to rapidly determine the resistance gene by mapping a new F
2
population that was developed from Tabasco and a susceptible cultivar “Ningmaizi119” and inoculated with pathogen isolate NCF-D-1–1 that was collected in the USA. The segregation of resistance in the population was found to link with
Pm2
which was identified in Tabasco. Therefore, it was concluded that the previously reported
Pm48
on chromosome arm 5DS in Tabasco should be the
Pm2
gene on the same chromosome. The
Pm2
was also found in European cultivars “Mattis” and “Claire” but not in any of the accessions from diploid wheat
Aegilops tauschii
or modern cultivars such as “Gallagher,” “Smith’s Gold,” and “OK Corral” being used in the Great Plains in the USA. A KASP marker was developed to track the resistance allele
Pm2
in wheat breeding.
The vertical distribution of fine particles with a diameter <inline-formula> <tex-math notation="LaTeX"> < 2.5~\mu \text{m} </tex-math></inline-formula> (PM 2.5 ) plays an important role in ...understanding the transport of air pollution and in making decisions regarding the prevention and control of regional air pollution. However, the studies of the vertical distribution of PM 2.5 were limited by the lack of monitoring data obtained with vertical sampling strategies. The lidar system can obtain the aerosol profile, which provides the possibility to measure PM 2.5 profile. Here, the vertical distributions of PM 2.5 concentrations were investigated on the basis of lidar data from January 2014 to October 2015. Linear regression, improved linear regression, and random forest (RF) models were used to retrieve the PM 2.5 concentration profile from lidar data. The models were built based on the relationship among extinction coefficient (EC), temperature (<inline-formula> <tex-math notation="LaTeX">T </tex-math></inline-formula>), relative humidity (RH), and surface PM 2.5 mass concentration. Comparison of the estimated and observed PM 2.5 showed that the RF model exhibited the best inversion effect. The correlation coefficient reached 0.75, and the root mean absolute error (RMAE) and root mean square error (RMSE) were 3.94 and 21.1 <inline-formula> <tex-math notation="LaTeX">\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>, respectively. Error analysis indicated that the estimated PM 2.5 retrieved using the linear and improved linear models (ILMs) was smaller than the observed PM 2.5 when EC was less than 0.7 km −1 , whereas PM 2.5 was evidently overestimated during winter pollution days. The reason might be that the effects of <inline-formula> <tex-math notation="LaTeX">T </tex-math></inline-formula> and RH were inaccurately considered. Finally, the seasonal variation of the PM 2.5 profiles was investigated. Results indicated that the mass concentration of PM 2.5 was relatively large within 0.5-1.5 km, with a maximum of 60 <inline-formula> <tex-math notation="LaTeX">\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>. The findings obtained here provide guidance for PM 2.5 vertical observation and regional pollutant transport.
PM2.5 is the key pollutant in atmospheric pollution in China. With new national air quality standards taking effect, PM2.5 has become a major issue for future pollution control. To effectively ...prevent and control PM2.5, its emission sources must be precisely and thoroughly understood. However, there are few publications reporting comprehensive and systematic results of PM2.5 source apportionment in the country. Based on PM2.5 sampling during 2009 in Shenzhen and follow-up investigation, positive matrix factorization（PMF） analysis has been carried out to understand the major sources and their temporal and spatial variations. The results show that in urban Shenzhen（University Town site）, annual mean PM2.5 concentration was 42.2 μg m？3, with secondary sulfate, vehicular emission, biomass burning and secondary nitrate as major sources; these contributed 30.0%, 26.9%, 9.8% and 9.3% to total PM2.5, respectively. Other sources included high chloride, heavy oil combustion, sea salt, dust and the metallurgical industry, with contributions between 2%–4%. Spatiotemporal variations of various sources show that vehicular emission was mainly a local source, whereas secondary sulfate and biomass burning were mostly regional. Secondary nitrate had both local and regional sources. Identification of secondary organic aerosol（SOA） has always been difficult in aerosol source apportionment. In this study, the PMF model and organic carbon/elemental carbon（OC/EC） ratio method were combined to estimate SOA in PM2.5. The results show that in urban Shenzhen, annual SOA mass concentration was 7.5 μg m？3, accounting for 57% of total organic matter, with precursors emitted from vehicles as the major source. This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in China.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Biomass burning is a significant source of fine particulate matter (PM2.5). Forest, bush, and peat fires in Kalimantan and Sumatra, Indonesia are major sources of transboundary haze pollution in ...Southeast Asia. However, limited data exist regarding the chemical characteristics of aerosols at sources. We conducted intensive field studies in Riau Province, Sumatra, Indonesia, during the peatland fire and non-burning seasons in 2012. We characterized PM2.5 carbonaceous aerosols emitted from peatland fire based on ground-based source-dominated sampling. PM2.5 aerosols were collected with two mini-volume samplers using Teflon and quartz fiber filters. Background aerosols were also sampled during the transition period between the non-burning and fire seasons. We analyzed the carbonaceous content (organic carbon (OC) and elemental carbon (EC)) by a thermal optical reflectance utilizing the IMPROVE_A protocol and the major organic components of the aerosols by a gas chromatography/mass spectrometry. PM2.5 aerosols emitted from peatland fire were observed in high concentrations of 7120 ± 3620 μg m−3 and were primarily composed of OC (71.0 ± 5.11% of PM2.5 mass). Levoglucosan exhibited the highest total ion current and was present at concentrations of 464 ± 183 μg m−3. The OC/EC ratios (36.4 ± 9.08), abundances of eight thermally-derived carbon fractions, OC/Levoglucosan ratios (10.6 ± 1.96), and Levoglucosan/Mannosan ratios (10.6 ± 2.03) represent a signature profile that is inherent in peatland fire. These data will be useful in identifying contributions from single or multiple species in atmospheric aerosol samples collected from peatland fires.
•PM2.5 aerosols emitted from peatland fire in Indonesia were characterized.•PM2.5 aerosols emitted from peatland fire were primarily composed of OC.•We found some source indicators that were inherent in peatland fire.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Fine particulate matter (PM 2.5 ) pollution can cause serious public health problems worldwide. A novel geographically and temporally weighted neural network constrained by global training (GC-GTWNN) ...is proposed in this article for the remote sensing estimation of surface PM 2.5 . The global neural network (NN) is trained to learn the overall effect of the influencing variables on surface PM 2.5 , and the local geographically and temporally weighted NN (GTWNN) addresses the spatiotemporal heterogeneity of the relationship between PM 2.5 and the influencing variables. Specifically, a global NN is trained with all samples collected from the entire study domain and period. Then, initialized with the global NN, the GTWNN models are built for each location and time and fine-tuned via spatiotemporally localized samples. Meanwhile, the geographically weighted loss function is designed for GTWNN. The proposed GC-GTWNN modeling is tested with a case study across China, which integrates satellite aerosol optical depth, surface PM 2.5 measurements, and auxiliary variables. Cross-validation results indicate that a remarkable improvement is observed from the global NN to GC-GTWNN modeling (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> value increasing from 0.49 to 0.80), and GC-GTWNN modeling also notably outperforms the conventionally popular PM 2.5 estimation models.
We assessed the health risks of fine particulate matter (PM2.5) ambient air pollution and its trace elemental components in a rural South African community. Air pollution is the largest environmental ...cause of disease and disproportionately affects low- and middle-income countries. PM2.5 samples were previously collected, April 2017 to April 2018, and PM2.5 mass determined. The filters were analyzed for chemical composition. The United States Environmental Protection Agency’s (US EPA) health risk assessment method was applied. Reference doses were calculated from the World Health Organization (WHO) guidelines, South African National Ambient Air Quality Standards (NAAQS), and US EPA reference concentrations. Despite relatively moderate levels of PM2.5 the health risks were substantial, especially for infants and children. The average annual PM2.5 concentration was 11 µg/m3, which is above WHO guidelines, but below South African NAAQS. Adults were exposed to health risks from PM2.5 during May to October, whereas infants and children were exposed to risk throughout the year. Particle-bound nickel posed both non-cancer and cancer risks. We conclude that PM2.5 poses health risks in Thohoyandou, despite levels being compliant with yearly South African NAAQS. The results indicate that air quality standards need to be tightened and PM2.5 levels lowered in South Africa.
Air monitoring network design is a critical issue because monitoring stations should be allocated properly so that they adequately represent the concentrations in the domain of interest. Although the ...optimization methods using observations from existing monitoring networks are often applied to a network with a considerable number of stations, they are difficult to be applied to a sparse network or a network under development: there are too few observations to define an optimization criterion and the high number of potential monitor location combinations cannot be tested exhaustively. This paper develops a hybrid of genetic algorithm and simulated annealing to combine their power to search a big space and to find local optima. The hybrid algorithm as well as the two single algorithms are applied to optimize an air monitoring network of PM2.5, NO2 and O3 respectively, by minimization of the mean kriging variance derived from simulated values of a chemical transport model instead of observations. The hybrid algorithm performs best among the algorithms: kriging variance is on average about 4% better than for GA and variability between trials is less than 30% compared to SA. The optimized networks for the three pollutants are similar and maps interpolated from the simulated values at these locations are close to the original simulations (RMSE below 9% relative to the range of the field). This also holds for hourly and daily values although the networks are optimized for annual values. It is demonstrated that the method using the hybrid algorithm and the model simulated values for the calculation of the mean kriging variance is of benefit to the optimization of air monitoring networks.
•Air monitoring network is optimized by minimization of the mean kriging variance.•We propose a hybrid of a genetic algorithm and simulated annealing.•No previous observation is needed as kriging variance is derived from simulations.•The hybrid algorithm outperforms the two single algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
PM 2.5 is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM 2.5 estimates using the newly ...released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM 2.5 data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM 2.5 data records in China. The PM 2.5 estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R 2 ) of 0.88, a root-mean-square error (RMSE) of <inline-formula> <tex-math notation="LaTeX">16.52~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>, and a mean absolute error of <inline-formula> <tex-math notation="LaTeX">10~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula> at the national scale. Spatiotemporal PM 2.5 variations at monthly scales are also well captured (e.g., <inline-formula> <tex-math notation="LaTeX">R^{2} =0.91 </tex-math></inline-formula>-0.94, RMSE = 5.8-<inline-formula> <tex-math notation="LaTeX">11.6~\mu \text{g}/\text{m}^{3}) </tex-math></inline-formula>. PM 2.5 varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM 2.5 pollution has reduced dramatically in China with an average of <inline-formula> <tex-math notation="LaTeX">- 5.6~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>/yr −1 during 2013-2018. Significant regional reductions are also seen, in particular, in the Beijing-Tianjin-Hebei region (<inline-formula> <tex-math notation="LaTeX">- 6.6~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>/yr −1 , <inline-formula> <tex-math notation="LaTeX">p < 0.001 </tex-math></inline-formula>), and the Deltas of Yangtze River (<inline-formula> <tex-math notation="LaTeX">- 6.3~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>/yr −1 , <inline-formula> <tex-math notation="LaTeX">p < 0.001 </tex-math></inline-formula>) and Pearl River Delta (<inline-formula> <tex-math notation="LaTeX">- 4.5~\mu \text{g}/\text{m}^{3} </tex-math></inline-formula>/yr −1 , <inline-formula> <tex-math notation="LaTeX">p < 0.001 </tex-math></inline-formula>). Our study improved the accuracy of near-surface PM 2.5 estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.
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