Impacts of particulate matter on Gulf of Mexico tropical cyclones

This research analyzes the relationship between tropical cyclones and fine particulate matter (PM_2.5) for landfalling Atlantic tropical cyclones from 2000 to 2015. Daily mean PM₂.₅ concentrations were collected from the United States Environmental Protection Agency. Tropical cyclone data were acquired from Tropical Prediction Center Best Track Reanalysis in Unisys weather. GRIdded Binary (GRIB formatted) data were downloaded from the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). Tracks of tropical cyclones were overlaid with the interpolated daily mean PM₂.₅ concentration value. Results suggest that, in general, tracks are distant from areas with the largest PM₂.₅ concentrations. To examine the cause-effect nature of this relationship, simulation using the Weather Research and Forecasting (WRF) model suggests that the intensity of Hurricane Lili was weakened only after passing the most PM₂.₅-polluted area in Louisiana. This result suggests that aerosol loading may weaken the intensity of tropical cyclones, at least in some cases.     

The evaluation of PM10, PM2.5, and PM1 concentrations during the Middle Eastern Dust (MED) events in Ahvaz,Iran, from april through september 2010

In this study, PM₁₀, PM_2.5, and PM₁ concentrations were measured from April through September 2010. These measurements were made every six days and on days with dust events using a Grimm Model 1.177 aerosol spectrometer. Meteorological data were also collected. Overall mean values of 319.6 ± 407.07, 69.5 ± 83.2, and 37.02 ± 34.9 μg/m³ were obtained for PM₁₀, PM_2.5, and PM₁, respectively, with corresponding maximum values of 5337.6, 910.9, and 495 μg/m³. The presence of the westerly prevailing wind implied that Iraq is the major source of dust events in this area. A total of 72 dust days and 711 dust hours occurred in the study area. The dust events occurred primarily during July. The longest dust event during the study period occurred in July, lasted five days, and had a peak concentration of 2028 μg/m³. These high concentrations produced AQI values of up to 500. A total estimated mortality and morbidity of 1131 and 8157 cases, respectively, can be attributed to these concentrations. The results of this study indicated the importance of dust events in Ahvaz and their possible health impacts. The study also demonstrated the need to design and implement intergovernmental management schemes to effectively mitigate such events.     

The PM10and PM2·5 dust generation potential of soils/sediments in the Southern Aral Sea Basin, Uzbekistan

The objective of this study was to assess the contribution of the major soil/sediment surfaces in the Southern Aral Sea Basin to the dust generation potential of this region. Eight crusts and soils/sediments from seven sites, representative of these surfaces, were sampled in the field and their major characteristics (particle size distribution, organic carbon content, carbonate content, salt content and composition) that are related to dust generation, were determined. The PM₁₀ and PM_2·5 dust generation potential of the materials was determined in the laboratory using the Lubbock Dust Generation, Analysis and Sampling System (LDGASS). The highest amount of PM₁₀ dust (579·3 mg.m⁻³) was generated from the Takyr crust material. The lowest by one Solonchak salt crust material (39·6 mg.m⁻³). Salt crusts from the desiccated Aral Sea bottom generated intermediate amounts of dust.The experimental results indicate that the Takyrs and Takyr-like soils, that occupy over 1 million ha in the Southern Aral Sea Basin, constitute the surfaces with the highest potential for being the source for the severe dust storms of the area. Second to the Takyr soils, the Solonchaks and Solonchak-like soils, also with an extent of over 1 million ha, contribute highly saline dust. To these must be added a large, as yet uncharted, proportion of the approximately 4 million ha of exposed sea bed, that exhibit Solonchak-like characteristics.     

From highly polluted inland city of China to “Lanzhou Blue”: The air-pollution characteristics

Air quality was improved considerably and the so-called "Lanzhou Blue" appeared frequently in Lanzhou due to implementation of some strict emission-control measures in recent years. To better understand whether the concentration of each air pollutant had decreased significantly and then give some suggestions as to urban air-quality improvement in the near future, the variations of the Air Quality Index (AQI) and six criterion air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) at five state-controlled monitoring sites of Lanzhou were studied from 2013 to 2016. The AQI, PM_2.5, PM₁₀, and SO₂ gradually decreased from 2013 to 2016, while CO and NO₂ concentrations had slightly increasing trends, especially in urban areas, due to the large number of motor vehicles, which had an annual growth rate of 30.87%. The variations of the air pollutants in the no-domestic-heating season were more significant than those in the domestic-heating season. The increase of ozone concentration for the domestic-heating season at a background station was the most significant among the five monitoring sites. The vehicle-exhaust and ozone pollution was increasingly severe with the rapid increase in the number of motor vehicles. The particulate-matter pollution became slight in the formerly highly polluted Lanzhou City. Some synergetic measures in urban and rural areas of Lanzhou should be taken by the local government in the near future to control fine particulate-matter (PM_2.5) and ozone pollution.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

Contribution of resuspension to particulate matter inmission levels in SE Spain

Ambient air particulate matter concentrations were measured at three locations in semi-arid SE Spain during 2005-2007. Sites representative of urban and rural background levels, as well as one representative of a rural area influenced by local mineral industry, were selected.The contribution of coarse particle resuspension (mainly crustal) in the area was assessed by studying the influence of wind speed, human activity and African dust outbreaks on the daily mass concentration and the aerosol number size distribution. Wind and soil characteristics in the area, typical of many semi-arid environments, are not conducive to major dust entrainment events.Twenty-four hour PM₁₀ mass concentrations, subjected to air quality regulation, present a net decrease as wind speeds increase at the three study sites. Size-resolved measurements in the diameter range 0.25-32 μm with higher temporal resolution, however, show a net increase in the coarse particle concentrations with increasing wind speed, while the smallest particles are diluted. Although suspension is found to occur at all wind speeds, threshold values for an increase in particulate concentration can be identified and show some dependence on the particle size.African dust outbreaks, human activity and wind speed are (in this order) the main contributors for increasing particle sizes.     

The cumulative characteristics of PAEs in PM2.5 in Changji,Northwest China

ABSTRACT

This paper discusses characteristics of the seasonal variation in phthalate acid esters (PAEs) in the PM_2.5 in the city of Changji, which is located an arid area of Xinjiang Province, northwest China. Samples were collected using a Laoying 2030 median discharge intelligent total suspended particulate (TSP) sampler and analyzed by a Shimadzu GC-2010 gas chromatograph. The results showed that PAEs in PM_2.5 were mainly composed of phthalic acid dibutyl phthalate (DBP) and phthalic acid (2-)ethyl hexyl ester (DEHP). DBP and DEHP are the most commonly used plasticizers, accounting for the majority of plasticizers used, and have large environmental emissions. Compared with the proportions of TSPs, proportions of DBP and DEHP in the PM_2.5 were lower, while the proportions of DMP and DEP were higher. A correlation exists between the distribution of PAEs and the particle size and concentration of atmospheric particulates. Seasonal variations in DBP and DEHP were the same, with lowest concentrations in the summer. Although seasonal variation in DMP was not the same as those of DBP and DEHP, the general trend was similar. Seasonal variation in DEP was different from those of other PAEs. This pattern is related to PAE characteristics and seasonal variations in their sources.     

Spatial Correlation between Traffic and Air Pollution in Beijing

Many studies have attempted to model the sophisticated influence of traffic emissions on air pollution, but most models only calculate the contribution of traffic emissions near monitoring sites. It is difficult to observe the near-surface dynamics such as wind, rain, and human activities and precisely distinguish traffic emissions. These obstacles make model simulation very expensive in practice. The regional distribution patterns that can help adjust policies and actions taken remain unknown. Therefore, this article proposes a grid-oriented geostatistics-based approach to overcome these obstacles. We chose central Beijing as the study area. An experiment implemented the approach on data collected from Global Positioning System navigation software, car rental companies, and meteorology stations. The results suggest that the northwest area of Beijing has high traffic-related air pollution (TRAP) and the southeast area has low TRAP. Unlike modeling-based methods, this work uses geostatistical methods to directly study the spatiotemporal connections between traffic and PM_2.5 (particulate matter with diameter less than 2.5?μm) from the phenomenon. The calculation is conducted under no hypotheses and has little risk of producing results contradictory to facts. This work provides a reference for future TRAP research on directly learning from the phenomenon and assists decision makers with seamless spatiotemporal heat maps of TRAP distribution. Key Words: Beijing, geospatial statistics, PM_2.5, spatial correlation, traffic-related air pollution.     

Analysis on the temporal-spatial distribution character and effect factors of PM10 in the hinterland of Taklimakan Desert and surrounding area

In recent years, the physical and chemical properties of dust aerosols from the dust source area in northern China have attracted increased attention. In this paper, Thermo RP 1400a was used for online continuous observation and study of the hinterland of Taklimakan, Tazhong, and surrounding areas of Kumul and Hotan from 2004 to 2006. In combination with weather analysis during a sandstorm in the Tazhong area, basic characteristics and influencing factors of dust aerosol PM₁₀ have been summarized as below: (1) The occurrence days of floating dust and blowing dust appeared with an increasing trend in Kumul, Tazhong and Hotan, while the number of dust storm days did not significantly change. The frequency and intensity of dust weather were major factors affecting the concentration of dust aerosol PM₁₀ in the desert. (2) The mass concentration of PM₁₀ had significant regional distribution characteristics, and the mass concentration at the eastern edge of Taklimakan, Kumul, was the lowest; second was the southern edge of the desert, Hotan; and the highest was in the hinterland of the desert, Tazhong. (3) High values of PM₁₀ mass concentration in Kumul was from March to September each year; high values of PM₁₀ mass concentration in Tazhong and Hotan were distributed from March to August and the average concentration changed from 500 to 1,000 g/m³, respectively. (4) The average seasonal concentration changes of PM₁₀ in Kumul, Tazhong and Hotan were: spring > summer > autumn > winter; the highest average concentration of PM₁₀ in Tazhong, was about 1,000 g/m³ in spring and between 400 and 900 g/m³ in summer, and the average concentration was lower in autumn and winter, basically between 200 and 400 g/m³. (5) PM₁₀ concentration during the sandstorm season was just over two times the concentration of the non-sandstorm season in Kumul, Tazhong and Hotan. The average concentrations of sandstorm season in Tazhong were 6.2 and 3.6 times the average concentrations of non-sandstorm season in 2004 and 2008, respectively. (6) The mass concentration of PM₁₀ had the following sequence during the dust weather: clear day < floating dust < floating and blowing dust < sandstorm. The wind speed directly affects the concentration of PM₁₀ in the atmosphere, the higher the wind speed, the higher the mass concentration. Temperature, relative humidity and barometric pressure are important factors affecting the strength of storms, which could also indirectly affect the concentration change of PM₁₀ in the atmosphere.     

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

     

An optimized spatial proximity model for fine particulate matter air pollution exposure assessment in areas of sparse monitoring

GIS-based proximity models are one of the key tools for the assessment of exposure to air pollution when the density of spatial monitoring stations is sparse. Central to exposure assessment that utilizes proximity models is the ‘exposure intensity–distance’ hypothesis. A major weakness in the application of this hypothesis is that it does not account for the Gaussian processes that are at the core of the physical mechanisms inherent in the dispersion of air pollutants.

Building upon the utility of spatial proximity models and the theoretical reliability of Gaussian dispersion processes of air pollutants, this study puts forward a novel Gaussian weighting function-aided proximity model (GWFPM). The study area and data set for this work consisted of transport-related emission sources of PM_2.5 in the Houston-Baytown-Sugar Land metropolitan area. Performance of the GWFPM was validated by comparing on-site observed PM_2.5 concentrations with results from classical ordinary kriging (OK) interpolation and a robust emission-weighted proximity model (EWPM). Results show that the fitting R² between possible exposure intensity calculated by GWFPM and observed PM_2.5 concentrations was 0.67. A variety of statistical evidence (i.e., bias, root mean square error [RMSE], mean absolute error [MAE], and correlation coefficient) confirmed that GWFPM outperformed OK and EWPM in estimating annual PM_2.5 concentrations for all monitoring sites. These results indicate that a GWFPM utilizing the physical dispersing mechanisms integrated may more effectively characterize annual-scale exposure than traditional models. Using GWFPM, receptors’ exposure to air pollution can be assessed with sufficient accuracy, even in those areas with a low density of monitoring sites. These results may be of use to public health and planning officials in a more accurate assessment of the annual exposure risk to a population, especially in areas where monitoring sites are sparse.     

Spatial-temporal characteristics of PM<Subscript>2.5</Subscript> in China: A city-level perspective analysis

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China’s PM_2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM_2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM_2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM_2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

2018年春季中国北方大范围沙尘天气对城市空气质量的影响及其天气学分析

利用多源卫星数据、空气质量数据和再分析资料ERA-Interim分析了2018年春季中国北方一次沙尘天气过程中沙尘气溶胶的光学特性及垂直结构、沙尘天气对颗粒物浓度及空气质量的影响及其机制。结果表明：（1）2018年3月27-30日中国北方地区气溶胶光学厚度、吸收性气溶胶光学厚度和气溶胶指数分别大于0.4、0.035和1.0。沙尘气溶胶主要分布在地面至8 km的范围内,且体积退偏比大部分在0.06~0.4,色比0.6~1.2。（2）各城市颗粒物浓度在一定程度上均受到沙尘天气的影响。3月27-28日,PM_2.5和PM₁₀的平均值分别是国家一级标准的4.1倍和4.3倍。29-30日,PM_2.5和PM₁₀的平均值分别是国家一级标准的3.5倍和3.4倍。28日河北张家口PM₁₀/PM_2.5达4 d之最,最大比值为10.9。（3）低层贝加尔湖高压南压对内蒙古西南部及河西走廊地区的起沙有重要影响,河套地区地面气旋和低层切变是造成华北西部起沙的关键因素,鄂海地区高压脊和东北地区地面的低压系统有利于东北地区的起沙。     

Urban Form,Air Pollution,and CO2 Emissions in Large U.S. Metropolitan Areas

In this article we explore the relationships between urban form and air pollution among 86 U.S. metropolitan areas. Urban form was quantified using preexisting sprawl indexes and spatial metrics applied to remotely sensed land cover data. Air pollution data included the nonpoint source emission of the ozone (O₃) precursors nitrogen oxides (NOx) and volatile organic compounds (VOCs), the concentration of O₃, the concentration and nonpoint source emission of fine particulate matter (PM2.5), and the emission of carbon dioxide (CO₂) from on-road sources. Metropolitan areas that exhibited higher levels of urban sprawl, or sprawl-like urban morphologies, generally exhibited higher concentrations and emissions of air pollution and CO₂ when controlling for population, land area, and climate.     

广州市人行天桥叶面滞尘粒度特征及其来源

对2009 年广州城区36 座人行天桥上采集的植被叶面滞尘样品进行粒度分析,粒度结果显示：叶面滞尘多为沙质粉沙,粉沙、沙和黏土体积分数的平均值分别为53.99%、41.34%和4.67%;粒度频率分布呈三峰型,主峰态在10~200 μm 之间。4 个市辖区叶面滞尘的粒度特征都较为相似,说明整个广州城区的叶面滞尘在成因和来源上都较为一致。叶面滞尘中的总悬浮颗粒物（TSP≤100 μm）体积分数极高,PM2.5 和PM10 体积分数略低;PM2.5/PM10 的平均值为0.36,推测其更多来自道路扬尘与建筑扬尘等局地污染源。风沙动力研究指示：其物源主要是由城区30km范围内短距离悬浮搬运而来,属内源性颗粒物。     

中国西部河谷型城市土壤与灰尘粒级组分分布特征

通过采集兰州市和延安市主城区的土壤灰尘样品,测量了其粒径组成,界定了土壤和灰尘不同粒径的空间分布,分析了环境影响.结果表明:兰州市和延安市城市土壤灰尘粒径组成有明显的分布特征,主要集中在小于100μm的部分,整体粒径分布情况为:粉黏粒(<50μm)>极细砂(50～100μm)>细砂(100～250μm)>中砂(250～...     

蒙古高原风蚀颗粒物空间过程数值模拟及预报

基于WRF-IWEMS耦合模型对2016年3月1~9日发生在蒙古高原的强沙尘天气过程进行数值模拟,着重模拟了尘源、粉尘传播路径以及粉尘扩散过程中浓度变化和影响范围,并采用卫星影像、站点监测数据与模型结果进行对比分析。结果表明：此次风沙天气过程的尘源分布在新疆哈密地区、阿拉善高原、中蒙边境戈壁地区以及浑善达克沙地部分地区,粉尘自源区分别沿河西走廊、贺兰山区、张家口等地扩散至华北和京津地区。蒙古高原土壤风蚀可使华北地区来自自然源的大气颗粒物PM₁₀、PM_2.5浓度分别达到1 000 μg·m^-3、200 μg·m^-3以上,还可使华北地区大气颗粒物浓度高于200 μg·m^-3的天气持续48 h以上。     

兰州市沙尘天气污染特征及潜在源区

为了解兰州市沙尘天气期间大气污染特征,选取2016—2017年兰州市国家大气环境监测网络系统21个监测站点逐小时数据,对沙尘天气过程中的污染物特征进行分析。在此基础上,结合地面气象参数,运用HYSPLIT模型聚类计算和激光雷达观测等手段,对颗粒物来源和传输过程进行潜在源区贡献因子（PSCF）和浓度权重轨迹（CWT）分析。结果表明：兰州沙尘天气主要受西北方向、东偏北短距离传输、北部内蒙古路径3类气团影响,其中西北方向气团影响沙尘天气持续时间较长,沙尘过程中消光系数最高,环境空气质量转差显著,其他两类气团影响沙尘天气持续时间较短,有利于污染扩散;沙尘天气发生前期PM₁₀与气态污染物呈现正相关,沙尘天气过程中为负相关,沙尘天气后则再次转为正相关,沙尘天气对气态污染物浓度有降低效果;春季兰州地区颗粒物潜在区分布相对集中,主要来自于兰州西北部河西走廊,PSCF值大于0.7;冬季颗粒物潜在源区分布范围较广,PSCF值集中在0.7~0.9,其中对PM₁₀较大贡献区集中在新疆东南部,贡献值240~320 μg·m^-3。     

Water content and land use history controlling soil CO2 respiration and carbon stock in savanna soil and groundnut fields in semi-arid Senegal

Decomposition of soil organic carbon (SOC) regulates the partitioning between soil C-stock and release of CO₂ to the atmosphere and is vital for soil fertility. Agricultural expansion followed by decreasing amounts of SOC and soil fertility is a problem mainly seen in tropical agro-ecosystems where fertilizers are in short supply. This paper focuses on factors influencing temporal trends in soil respiration measured as CO₂ effluxes in grass savanna compared with groundnut (Arachis hypogaea L.) fields in the semi-arid part of Senegal in West Africa. Based on laboratory experiments, soil CO₂ production has been expressed as a function of temperature and soil water content by fit equations. Field measurements included soil CO₂ effluxes, soil temperatures and water contents. Effluxes in grass savanna and groundnut fields during the dry season were negligible, while effluxes during the rainy season were about 3–8 μmol CO₂ m^?2 s^?1, decreasing to less than 1 μmol by the end of the growing season. Annual soil CO₂ production was simulated to be in the range of 31–38 mol C m^?2. Furthermore, a controlled water addition experiment revealed the importance of rain during the dry season for the overall turnover of soil organic matter.     

Deriving hillslope sediment budgets in wildfire-affected forests using fallout radionuclide tracers

Information on post-fire sediment and nutrient redistribution is required to underpin post-fire catchment management decisions. Fallout radionuclide budgets (²¹⁰Pb_xs, ¹³⁷Cs and ⁷Be) were derived to quantify soil redistribution and sediment yield in forested terrain following a moderately severe wildfire in a small (89 ha) water supply catchment in SE Australia. Application of these techniques in burnt terrain requires careful consideration of the partitioning of radionuclides between organic and mineral soil components. Beryllium-7 and ²¹⁰Pb_xs were shown to be closely associated with ash, litter and soil organic matter whereas ¹³⁷Cs was more closely associated with subsurface coarse mineral soil. Comparison of the three tracer budgets indicated that the dominant sediment source areas were ridgetops and steep valley sideslopes, from which burnt surface material was conveyed to the stream network via pre-existing gullies. Erosion was predominantly driven by sheetwash, enhanced by soil water repellency, and modified by bioturbation which both supplies subsurface sediment and provides sinks for erosive overland flow. Footslope and riparian zones were not important sediment source areas. The estimated event-based (wildfire and subsequent rainfall) sediment yield is 58 ± 25 t km^− 2, based on fallout ⁷Be measurements. The upper estimate of total particulate phosphorus yield (0.70 kg ha^− 1) is more than 10 times that at equivalent unburnt sites. This illustrates that, soon after fire, burnt eucalypt forest can produce nutrient loads similar to those of agricultural catchments. The tracer budgets indicate that wildfire is an important control on sediment and phosphorus inputs to the stream network over the decadal timeframe and the pulsed nature of this release is an important concern for water quality management.