Dry nitrogen deposition estimates over a forest experiencing free air CO2 enrichment

The quantification of atmospheric additions of nitrogen (N) to an ecosystem is often desirable, but difficult for many locations including many ecological manipulation experiments. Ideal methodologies for the complete and chemically speciated quantification of dry N deposition (e.g. tunable diode laser absorption spectrometer, thermal‐dissociation laser‐induced fluorescence) are expensive, technologically challenging to maintain, and rarely colocated with important global change manipulation experiments. Here we present an alternative method for obtaining an approximation of total N deposition using short‐term eddy flux and concentration measurements, annual regional concentration estimates, and modeling for the Duke Experimental Forest. The motivation for generating estimates for this location was to inform the long‐term elevated CO₂ experiment conducted at Duke Forest. We estimated the total annual atmospheric N deposition to the forest to be 13.7 kg N ha^?1. Of this total, ～58% was in dry‐deposited forms. Surprisingly, and contrary to some previous predictions, nitric acid (HNO₃) was not the dominant portion of the total dry‐deposited N, implying strongly that other forms of gaseous N like organic peroxy and alkyl nitrate compounds are a significant portion of the total flux. Furthermore, this study sheds light on the completeness of the estimates derived from Clean Air Status and Trends Network (CASTNet) and other dry deposition networks. CASTNet does not measure organic forms of dry deposition. In fact, CASTNet only quantifies HNO₃ in the gas phase and may significantly underestimate total N deposition in many environments.     

Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction

Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents’ travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability.     

街谷绿化消减机动车排放污染物的研究方法与进展

随着街道两侧建筑群向高密度、高层化发展,传统“树越多越好”的绿化模式在应对城市街谷空气污染时是否适用受到广泛关注。明晰街谷绿化植物特征及其种植设计模式对机动车排放污染物消减的调控机理是发挥其提升街谷空气品质的重要前提。本文通过对近年国内外相关文献的梳理,比较分析了现场观测、风洞试验和数值模拟3种研究方法的适用性及局限性,详细剖析了行道树、绿篱两类绿化模式对街谷空气污染物扩散与沉降的影响机制,提出了一套面向空气质量提升的街谷绿化适应性设计工作框架。最后,分别从典型街谷绿化设计图示语言研制、街谷空气污染暴露评估技术指南编制、计算机流体力学模型中街谷绿化效应物理过程参数化方案优化等方面进行探讨,以期为后续研究提供思路和借鉴。     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Air pollution and watershed research in the central Sierra Nevada of California: nitrogen and ozone

Maintaining healthy forests is the major objective for the Forest Service scientists and managers working for the U.S. Department of Agriculture. Air pollution, specifically ozone (O3) and nitrogenous (N) air pollutants, may severely affect the health of forest ecosystems in the western U.S. Thus, the monitoring of air pollution concentration and deposition levels, as well as studies focused on understanding effects mechanisms, are essential for evaluation of risks associated with their presence. Such information is essential for development of proper management strategies for maintaining clean air, clean water, and healthy ecosystems on land managed by the Forest Service. We report on two years of research in the central Sierra Nevada of California, a semi-arid forest at elevations of 1100-2700 m. Information on O3 and N air pollutants is obtained from a network of 18 passive samplers. We relate the atmospheric N concentration to N concentrations in streams, shallow soil water, and bulk deposition collectors within the Kings River Experimental Watershed. This watershed also contains an intensive site that is part of a recent Forest Service effort to calculate critical loads for N, sulfur, and acidity to forest ecosystems. The passive sampler design allows for extensive spatial measurements while the watershed experiment provides intensive spatial data for future analysis of ecosystem processes.     

Air quality and its response to satellite-derived urban form in the Yangtze River Delta,China

Air pollution is one of the top environmental concerns and causes of deaths and various diseases worldwide. An important question for sustainable development is to what extent urban design can improve or degrade urban air quality. In this article, we explored the relationship between ground-based observations of air pollution and urban form in the Yangtze River Delta (YRD), the largest metropolitan region in China. We analyzed six criteria pollutants (SO₂, NO₂, PM₁₀, PM_2.5, CO, O₃) and summarized metric (air quality index, AQI) from 129 ambient air quality monitoring stations during 2015. Urban form was characterized using six spatial metrics, including the size, shape, regularity, fragmentation and traffic coupling factor of urban patches, based on satellite-derived land cover data. The results indicated that: (1) PM_2.5, PM₁₀ and O₃ were three primary pollutants in the YRD. The annual average AQI was 79, and the air quality was “moderate” for human health, with the highest and lowest AQI appeared in winter (107) and summer (60). Moreover, the air quality of the southern areas (Zhejiang province, AQI: 68) was generally better than the northern parts (Jiangsu province, AQI: 86). (2) Through the size and shape of urban patches, urban form had a significant effect on urban air quality in the YRD. PARA_MN (Mean Perimeter-area ratio), ENN_MN (Mean Euclidean Nearest Neighbor Distance), CA (Total Urban Area) and NP (Number of urban patches) had the most significant impacts on air quality. PM₁₀ and PM_2.5 were two important pollutants highly positively related to CA and NP, while negatively related to PARA_MN and ENN_MN. In addition, the polycentric urban form was associated with high air quality. (3) Land use configuration was an important indicator to describe the urban air quality. When buffer distance of spatial scale was 25 km, air quality showed the highest correlation with forest coverage. A high forest coverage rate contributed to the better air quality, increasing or preserving the forested areas would help mitigate the air pollution.     

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.     

Estimation of Citywide Air Pollution in Beijing

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM_2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM_2.5 observation station of the U.S Embassy and the 18 PM₁₀ observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM_2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM_2.5 monitoring station and at the 18 official PM₁₀ stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM_2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.     

空气污染对信鸽比赛个体归巢速度的影响

空气污染作为一种有害的环境因素,对人类及动物的生理、心理均有影响.在鸟类中,信鸽(Columba livia)是研究空气污染影响的理想模型.为探究空气污染的行为学效应,通过收集并筛选2018和2019年成都市信鸽协会春秋两个季节举办的64场赛事共285羽参赛5场及以上的信鸽不同空距等级下的归巢速度,利用混合线性模型分析...     

国家重点生态功能区县域环境空气质量时空变化分析

评估国家重点生态功能区县域空气质量改善状况,对落实国家生态文明建设与重点生态功能区转移支付制度具有现实意义。以国家重点生态功能区转移支付县域空气质量为评价对象,基于2015年至2019年的空气质量监测站点日均值数据,对环境空气质量状况和变化进行了评价分析。研究结果表明：国家重点生态功能区转移支付县域空气质量总体表现较好且仍在稳步提升,转移支付政策对2015—2019年国家重点功能区空气质量的改善有着明显的推动作用。优良天数比例超过90%的县域占比由2015年的33.77%提高到2019年的69.52%,水源涵养区的优良天数占比由2015年的72.71%提高到2019年的92.09%,空气质量改善效果最为显著。2015—2019年间,细颗粒物(PM_2.5)污染占比明显下降,臭氧(O₃)污染占比大幅上升,主要超标污染物由颗粒物逐渐转为颗粒物和臭氧。当前防风固沙区的主要超标污染物为可吸入颗粒物(PM₁₀)和细颗粒物,其他三类生态功能区为细颗粒物和臭氧。重点生态功能区的空气质量存在着显著的空间差异,空气污染表现出显著的空间聚集特...     

Ecosystem classification and inventory maps as surrogates for ground beetle assemblages in boreal forest

Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle (Coleoptera: Carabidae) biodiversity patterns that are related to the forest canopy mosaic. Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data.Methods We used a systematic sampling grid of 198 sites, covering 84 km 2 of boreal mixedwood forest in northwestern Alberta, Canada. For every site, we determined tree basal area, characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information. We used variation partitioning, ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation.Important findings Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns. The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models. Compared to field survey data, air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures. This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover. Combination of relative canopy cover from provincial inventory with other geo-referenced land variables to produce the ecosystem classification maps improved biodiversity predictive power. The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management. In order to complement conservation efforts established in protected areas, accurate, high resolution, wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest. In development of these strategies, an in-depth understanding of vegetation is key.     

Impacts of acid deposition,ozone exposure and weather conditions on forest ecosystems in Europe: an overview

Background

In 1994, a “Pan-European Programme for Intensive and Continuous Monitoring of Forest Ecosystems” started to contribute to a better understanding of the impact of air pollution, climate change and natural stress factors on forest ecosystems. The programme today counts approximately 760 permanent observation plots including near 500 plots with data on both air quality and forest ecosystem impacts.

Scope

This paper first presents impacts of air pollution and climate on forests ecosystems as reported in the literature on the basis of laboratory and field research. Next, results from monitoring studies, both at a European wide scale and related national studies, are presented in terms of trends and geographic variations in nitrogen and sulphur deposition and ozone concentrations and the impacts of those changes in interaction with weather conditions on (i) water and element budgets and nutrient-acidity status, (ii) forest crown condition, (iii) forest growth and carbon sequestration and (iv) species diversity of the ground vegetation. The empirical, field based forest responses to the various drivers are evaluated in view of available knowledge.

Conclusions

Analyses of large scale monitoring data sets show significant effects of atmospheric deposition on nutrient-acidity status in terms of elevated nitrogen and sulphur or sulphate concentrations in forest foliage and soil solution and related soil acidification in terms of elevated aluminium and/or base cation leaching from the forest ecosystem. Relationships of air pollution with crown condition, however, appear to be weak and limited in time and space, while climatic factors appear to be more important drivers. Regarding forest growth, monitoring results indicate a clear fertilization effect of N deposition on European forests but the field evidence for impacts of ambient ozone exposure on tree growth is less clear.     

Temperature, air pollution and total mortality during summers in Sydney, 1994–2004

This study investigated the effect of temperature and air pollutants on total mortality in summers in Sydney, Australia. Daily data on weather variables, mortality and air pollution for the Sydney metropolitan area from 1 January 1994 to 31 December 2004 were supplied by Australian Bureau of Meteorology, Australian Bureau of Statistics, and Environment Protection Agency of New South Wales, respectively. We examined the association of total mortality with weather indicators and air pollution using generalised additive models (GAMs). A time-series classification and regression tree (CART) model was developed to explore the interaction effects of temperature and air pollution that impacted on mortality. Our results show that the average increase in total daily mortality was 0.9% [95% confidence interval (CI): 0.6-1.3%] and 22% (95% CI: 6.4-40.5%) for a 1 degrees C increase in daily maximum temperature and 1 part per hundred million (pphm) increase in daily average concentration of sulphur dioxide (SO(2)), respectively. Time-series CART results show that maximum temperature and SO(2) on the current day had significant interaction effects on total mortality. There were 7.3% and 12.1% increases in daily average mortality when maximum temperature was over 32 degrees C and mean SO(2) exceeded 0.315 pphm, respectively. Daily maximum temperature was statistically significantly associated with daily deaths in Sydney during summers between 1994 and 2004. Elevated daily maximum temperature combined with high SO(2) concentrations appeared to have contributed to the increased mortality observed in Sydney during this period.     

Spatially Explicit Characterization of Acidifying and Eutrophying Air Pollution in Life-Cycle Assessment

Abstract: Simple models are often used to assess the potential impact of acidifying and eutrophying substances released during the life cycle of products. As fate, background depositions, and ecosystem sensitivity are not included in these models, environmental life-cycle assessment of products (LCA) may produce incorrect results for these impact categories. This paper outlines the spatially explicit regional air pollution information and simulation model (RAINSLCA), which was developed for the calculation of acidification and terrestrial eutrophication potentials of ammonia (NH3) and nitrogen oxide (NOx) air emissions and acidification potentials for sulfur dioxide (SO2) air emissions for Europe and a number of European regions, taking fate,     

Pollutant dispersion in the urban environment

Flow and pollutant dispersion models are important elements for managing air quality in urban areas, to complement and, sometimes, even substitute monitoring. Developing fast and reliable parameterisations is necessary to improve the spatial and temporal resolutions of current mathematical prediction models. Recently there has been a growing interest in the so-called “neighbourhood scale” models, that offer relatively high spatial and temporal resolutions while keeping the needed computational resources at a minimum. This paper describes experimental and numerical simulations performed to explore the interaction of flow and pollutant dispersion with local building and street geometry. The methods developed may be useful as a way for cities to improve air quality management.     

Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes

Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because (1) changes in air quality are intermediates on the causal pathway between regulation and health, (2) regulations typically affect multiple pollutants on the causal pathway towards health, and (3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.     

A novel downscaling approach to predict plant invasions and improve local conservation actions

To successfully protect native biodiversity from the effects of biological invasions, local conservation priorities must be established. For this purpose, fine-grained species distribution data is required but often unavailable. We present a new approach to obtain fine-grained predictions of invasion through the development of downscaled invasion maps based on coarse-grained distribution data. The framework is illustrated for the alien invader Acacia dealbata in the Northwest of Portugal. The analytical design was divided in five steps: (1) three individual coarse-grained models were calibrated and their spatial predictions were downscaled into fine-grained models using three different downscaling techniques; (2) a Downscaling Consensus Map was built by spatially combining the predictions from those three models; (3) using coarse-grained (1 km²) or fine-grained (0.04 km²) datasets, two different models were fitted and spatially projected; (4) for each spatial resolution, Conservation Value maps were produced, based on the spatial combination of the protection networks represented in the region; and (5) the spatial conflicts between the predicted distribution of the invader and Conservation Value maps were calculated and compared for the several invasion maps. The downscaled models showed high predictive performance (AUC > 0.9). The spatial projections of the different models revealed a general similarity among projections from all modelling techniques, for both the patterns of invasion and the conflicts with conservation areas. The possibility of obtaining detailed and reliable predictions based on coarse-grained distribution data could avoid costly fieldwork to collect fine-grained distribution data while effectively supporting the management of invasions at the appropriate scales.     

东江流域景观格局对氮、磷输出的影响

基于实地采样水质数据与遥感解译所得土地利用数据,采用相关分析和冗余分析,从时间和空间两个尺度,探讨东江流域景观格局与氮、磷输出的关系。结果表明：（1）城镇和水域面积占比与河流氮磷含量正相关,林地和草地面积占比与河流氮磷含量负相关。（2）景观分裂指数（DIVISION）、香浓多样性指数（SHDI）、散布与并列指数（IJI）、边缘密度（ED）和斑块密度（PD）等景观指数与河流氮磷含量呈正相关关系,最大斑块指数（LPI）、蔓延度指数（CONTAG）和相似邻近比例（PLADJ）与河流总氮总磷含量负相关。（3）平水期河流水质与景观格局的相关性强于丰水期;从空间尺度来看,集水区尺度与河流水质的相关性好于缓冲区尺度;此外,总氮对于景观格局的响应比总磷更敏感。     

Bayesian spatiotemporal modeling on complex-valued fMRI signals via kernel convolutions

We propose a model-based approach that combines Bayesian variable selection tools, a novel spatial kernel convolution structure, and autoregressive processes for detecting a subject's brain activation at the voxel level in complex-valued functional magnetic resonance imaging (CV-fMRI) data. A computationally efficient Markov chain Monte Carlo algorithm for posterior inference is developed by taking advantage of the dimension reduction of the kernel-based structure. The proposed spatiotemporal model leads to more accurate posterior probability activation maps and less false positives than alternative spatial approaches based on Gaussian process models, and other complex-valued models that do not incorporate spatial and/or temporal structure. This is illustrated in the analysis of simulated data and human task-related CV-fMRI data. In addition, we show that complex-valued approaches dominate magnitude-only approaches and that the kernel structure in our proposed model considerably improves sensitivity rates when detecting activation at the voxel level.