Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

Temporal scale-induced uncertainty in load duration curves for instream-dissolved oxygen

Load duration curves were developed using the Hydrological Simulation Program FORTRAN (HSPF) for dissolved oxygen (DO) for the Amite River in Louisiana, USA. The concept of ‘dissolved oxygen reserve’, defined as the total quantity of DO, is introduced. The effect of temporal resolution on duration curves of DO reserve was examined using duration curves developed based on daily, weekly, biweekly, and monthly average data. Duration curves for DO exhibited high variability in the load estimated using daily data as compared to those based on biweekly and monthly data. A seasonal analysis revealed the trend in the DO reserve. The daily DO reserve for the Amite River at Port Vincent was 44,049.31 kg when daily summer data were used and 74,255.15 kg for daily annual data. A surplus of 10,691 kg of DO reserve was shown in the monthly data during critical summer months. The coefficient of variation (CV), used to define the temporal scale-induced uncertainty, was found to be linearly and inversely correlated with the logarithm of the time scale. Regression equations were developed to extrapolate near real-time flow and water quality data, greatly simplifying flow and water quality monitoring and reducing the cost involved in flow and water quality monitoring.     

基于集合经验模态分解和支持向量机的溶解氧预测

应用集合经验模态分解(EEMD)和支持向量机(SVM)相结合的方法,建立一种天然水体溶解氧浓度预测模型。首先,利用EEMD方法将溶解氧时序分解成不同频段的分量,以降低序列的非平稳性;然后,根据各序列分量的自身特征建立合适的SVM预测模型,此过程通过相关分析确定各分量输入量;最后,将各子分量预测值合成得到最终的预测结果。使用该模型对嘉陵江北温泉段的溶解氧浓度进行预测,结果表明,与传统单一的SVM和BP神经网络模型相比,该模型能有效提高预测精密度,具有良好的应用前景。     

Assessment of physicochemical parameters of wastewater samples

Water samples from selected locations of Nullah Lai and Koh-e-Noor textile mill in the metropolitan city of Rawalpindi and Islamabad, Pakistan, were collected. Physicochemical parameters and heavy metals were determined using standard analytical procedures in comparison with sites, locations and subsequent interval of 3 months. The results of the physicochemical analysis at different locations of Nullah Lai and Koh-e-Noor textile mill with an interval of 3 months were obtained in the following range: pH (7.16–8.29), temperature (17.8–28.8 °C), conductivity (1,005–3,347 μS/m), TDS (754.3–2,519.5 mg/L), turbidity (272.8–487.05 NTU), total hardness (300–452 mg/L), nitrates (10.11–22.95 ppm), calcium (74.31–139.2 ppm), chloride (127.72–396.16 ppm), sulphate (15.97–87.38 ppm), NaCl (210.5–631.1 ppm), Ni (0.30–0.72 ppm), Cd (0.005–0.03 ppm), Cr (0.2–7.4 ppm), Pb (0.12–0.73 ppm), Zn (0.03–0.08 ppm) and Cu (0.01–0.06 ppm). The highest value of physicochemical parameters (compared with Nullah Lai) was obtained in locations of Koh-e-Noor textile mill. The results obtained exceeded the maximum allowable limit set by the World Health Organization for drinking purpose but can be used for irrigation purposes after suitable treatment and purification.     

Mortality factors and lead contamination of wild birds from Korea

Wild birds have frequently been found dead in their natural habitats, but little is known about what ecological stressors may impact health of wild populations. Here, we report the potentially harmful lead (Pb) levels in tissues along with necropsies on 69 individuals of cranes, raptors, and waterfowl found dead between 2000 and 2003 in Korea. In all samples diagnosed, trauma (n?=?22), severe emaciation (n?=?15), and infectious diseases (n?=?11) were identified. In the survey, injury with Pb shot or bullet fragments was associated with three of the deaths; one of three showed lesions suggestive of Pb poisoning in the tissues. Of 69 birds, 12 had >25 ppm dry wt. (equivalent to 8 ppm wet wt.) in liver or kidney, which is known to be a potentially lethal level of Pb in wild birds. Three individuals had hepatic Pb levels of 101.3 ppm (Whooper swan), 120.4 ppm (Great white-fronted goose), and 1,059 ppm (Mandarin duck), with evidence of Pb pellets in their gizzard. This study suggests that many birds examined may be suffering from excessive Pb exposure that may be of health concern with respect to a potential cause of their mortality. The need for additional research is heightened when considering that some migrants are classified as a globally protected species by the International Union for the Conservation of Nature.     

Nutrient baselines of Cerrado low-order streams: comparing natural and impacted sites in Central Brazil

The aquatic systems responsible for water supply in the Brazilian Federal District (FD) have been threatened by anthropogenic pressures, especially considering the expressive demographic increase in the region during the last decades. The purposes of this research were: (a) to assess the water quality in streams located in the FD by monitoring physical–chemical variables; (b) to define baselines for these variables among different ecological status categories. The 14 investigated streams were sampled between 2006 and 2009, in the dry (August–September, 2006, 2008, 2009) and rainy (March–April, 2008, 2009) seasons. All sampling sites were classified in four categories (“very impacted”, “impacted”, “in transition” and “natural”) using an adaptation of a rapid habitat assessment protocol. Differences in water quality among sites were generally well predicted in the four ecological status categories defined by the protocol, which showed a gradient in nutrient concentrations from reference sites classified as “natural” (medians: electrical conductivity?=?7.3 μS cm^?1; nitrate?=?0.040 mg L^?1; ammonium?=?0.039 mg L^?1; soluble reactive phosphorus (SRP)?=?<0.001 mg L^?1; total phosphorus (TP)?=?0.006 mg L^?1; ) to those classified as “very impacted” (medians: electrical conductivity?=?87.7 μS cm^?1; nitrate?=?0.247 mg L^?1; ammonium?=?0.219 mg L^?1; SRP?=?0.010 mg L^?1; TP?=?0.035 mg L^?1). Point sources inputs were the main factor for water quality deterioration. The nutrient baselines reported were relatively low when compared to data collected from reference areas in Brazil (e.g., São Paulo State) or temperate regions, especially for TP.     

Blood levels of cadmium and lead in residents near abandoned metal mine areas in Korea

We analyzed national data on blood lead levels (BLL) and blood cadmium levels (BCL) in residents living near 38 abandoned metal mining areas (n?=?5,682, 18–96 years old) in Korea that were collected by the first Health Effect Surveillance for Residents in Abandoned Metal mines (HESRAM) from 2008 to 2011. The geometric mean BCL and BLL were 1.60 μg/L (95 % CI?=?1.57–1.62 μg/L) and 2.87 μg/dL (95 % CI?=?2.84–2.90 μg/dL), respectively, notably higher than levels in the general population in Korea and other countries. We found significantly higher BLL and BCL levels in people living within 2 km of an abandoned metal mine (n?=?3,165, BCL?=?1.87 μg/L, BLL?=?2.91 μg/dL) compared to people living more than 2 km away (n?=?2,517, BCL?=?1.31 μg/L, BLL?=?2.82 μg/dL; P?<?0.0001) and to the general population values reported in the literature.     

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl^?), calcium (Ca²⁺), total alkalinity, total hardness, nitrate nitrogen (NO₃-N), and ammonical nitrogen (NH₄-N). The ANN structure with 14 hidden neurons obtained the best selection. By making comparison between the results of the ANN model and the measured data on the basis of correlation coefficient (r) and root mean square error (RMSE), a good model-fitting DO values indicated the effectiveness of neural network model. It is found that the coefficient of correlation (r) values for the training, validation, and test sets were 0.9654, 0.9841, and 0.9680, respectively, and the respective values of RMSE for the training, validation, and test sets were 0.4272, 0.3667, and 0.4570, respectively. Sensitivity analysis was used to determine the influence of input variables on the dependent variable. The most effective inputs were determined as pH, NO₃-N, NH₄-N, and Ca²⁺. Cl^? was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.     

Calibration and verification of a dissolved oxygen management model for a highly polluted river with extreme flow variations in Pakistan

A dissolved oxygen (DO) model is calibrated and verified for a highly polluted River Ravi with large flow variations. The model calibration is done under medium flow conditions (431.5 m³/s), whereas the model verification is done using the data collected during low flow conditions (52.6 m³/s). Biokinetic rate coefficients for carbonaceous biochemical oxygen demand (CBOD) and nitrogenous biochemical oxygen demand (NBOD) (i.e, K _cr and K _n ) are determined through the measured CBOD and ammonia river profiles. The calculated values of K _cr and K _n are 0.36 day^?1 and 0.34 day^?1, respectively. The close agreement between the DO model results and the field values shows that the verified model can be used to develop DO management strategies for the River Ravi. The biokinetic coefficients are known to vary with degree of treatment (DOT) and therefore need to be adjusted for a rational water quality management model. The effect of this variation on level of treatment has been evaluated by using the verified model to attain a DO standard of 4 mg/L in the river using the biokinetic rate coefficients as determined during the model calibration and verification process. The required DOT in this case is found to be 96 %, whereas the DOT is 86 % if adjusted biokinetic rate coefficients are used to reflect the effect of wastewater treatment. The cost of wastewater treatment is known to increase exponentially as the removal efficiency increases; therefore, the use of appropriate biokinetic coefficients to manage the water quality in rivers is important.     

Occurrence and sorption properties of arsenicals in marine sediments

The content of total arsenic, the inorganic forms: arsenite (As(III)) and arsenate (As(V)), the methylated forms: monomethylarsonic acid and dimethylarsinic acid (DMA), trimethylarsenic oxide, tetramethylarsenonium ion and arsenobetaine was measured in 95 sediment samples and 11 pore water samples from the Baltic Sea near the island of Bornholm at depths of up to 100 m. As(III+V) and DMA were detected in the sediment and As(III+V) was detected in the sediment pore water. Average total As concentration of 10.6?±?7.4 mg/kg dry matter (DM) in the sediment corresponds to previously reported values in the Baltic Sea and other parts of the world. Existing data for on-site measurements of sorption coefficients (Kd) of arsenicals in marine and freshwater sediments show large variability from <1 to >1,000 L/kg. In this work, calculated sorption coefficients (Kd and Koc) for As(III+V) showed significant correlation with depth, dissolved oxygen (DO), salinity and sediment classification; for depths <70 m, salinity <11 %, DO >9 mg/L and sand/silt/clay sediments the Kd was 118?±?76 L/kg DM and for depths >70 m, salinity >11 %, DO?<?9 mg/L and muddy sediments the Kd was 513?±?233 L/kg DM. The authors recommend using the found Kd value for arsenic in marine sediments when conditions are similar to the Baltic Sea. At locations with significant anthropogenic point sources or where the local geology contains volcanic rock and sulphide mineral deposits, there may be significantly elevated arsenic concentrations, and it is recommended to determine on-site Kd values.     

Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling.     

Identifying dissolved oxygen variability and stress in tidal freshwater streams of northern New Zealand

Tidal streams are ecologically important components of lotic network, and we identify dissolved oxygen (DO) depletion as a potentially important stressor in freshwater tidal streams of northern New Zealand. Other studies have examined temporal DO variability within rivers and we build on this by examining variability between streams as a basis for regional-scale predictors of risk for DO stress. Diel DO variability in these streams was driven by: (1) photosynthesis by aquatic plants and community respiration which produced DO maxima in the afternoon and minima early morning (range, 0.6-4.7 g/m(3)) as a product of the solar cycle and (2) tidal variability as a product of the lunar cycle, including saline intrusions with variable DO concentrations plus a small residual effect on freshwater DO for low-velocity streams. The lowest DO concentrations were observed during March (early autumn) when water temperatures and macrophyte biomass were high. Spatial comparisons indicated that low-gradient tidal streams were at greater risk of DO depletions harmful to aquatic life. Tidal influence was stronger in low-gradient streams, which typically drain more developed catchments, have lower reaeration potential and offer conditions more suitable for aquatic plant proliferation. Combined, these characteristics supported a simple method based on the extent of low-gradient channel for identifying coastal streams at risk of DO depletion. High-risk streams can then be targeted for riparian planting, nutrient limits and water allocation controls to reduce potential ecological stress.     

Improving Estimation of Specific Surface Area by Artificial Neural Network Ensembles Using Fractal and Particle Size Distribution Curve Parameters as Predictors

Specific surface area (SSA) is one of the principal soil properties used in modeling soil processes. In this study, artificial neural network (ANN) ensembles were evaluated to predict SSA. Complete soil particle-size distribution was estimated from sand, silt, and clay fractions using the model by Skaggs et al. and then the particle-size distribution curve parameters (PSDCPs) and fractal parameters were calculated. The PSDCPs were used to predict 20 particle-size classes for a soil sample’s particle size distribution. Fractal parameters were calculated by the model of Bird et al. In addition, total soil-specific surface area (TSS) was calculated using the above 20 size classes. Pedotransfer functions were developed for SSA and TSS using ANN ensembles from 63 pieces of SSA data taken from the literature. Fractal parameters, PSDCPs, and some other soil properties were used to predict SSA and TSS. Introducing fractal parameters and PSDCPs improved the SSA estimations by 12.5 and 11.1 %, respectively. The improvements were even better for TSS estimations (27.7 and 27.0 %, respectively). The use of fractal parameters as estimators described 44 and 92.8 % of the variation in SSA and TSS, respectively, while PSDCPs explained 42 and 6.6 % of the variation in SSA and TSS, respectively. The results suggested that fractal parameters and PSDCPs could be successfully used as predictors in ANN ensembles to predict SSA and TSS.     

Pristine aquatic systems in a Long Term Ecological Research (LTER) site of the Brazilian Cerrado

The maintenance of limnological monitoring programs in the Cerrado Domain is crucial as a provision of useful information about temporal variations in land use and their respective water quality responses, considering its importance as water source for different Brazilian hydrographic basins. The purpose of this research was to describe limnological variables of low-order lotic systems located in the Cerrado Long Term Ecological Research (LTER) site (Environmental Protection Area (APA) Gama and Cabeça de Veado, Federal District of Brazil). Altogether, nine different streams were considered in this study. Samplings were conducted between 2010 and 2012, concentrated in the dry and rainy seasons. The sampling sites were generally characterized by low nutrient concentrations (e.g., medians, TP?=?14.8 μg L^?1, TN?=?20.0 μg L^?1, NO₃?=?13.8 μg L^?1) and slightly acidic waters (median, pH?=?5.3), with quite low electrical conductivity values (median?=?6.4 μS cm^?1). However, water quality degradation as a response to diffuse pollution was reported in some sampling points (e.g., Onça and Gama streams), expressed by relatively higher N and P concentrations, which were probably highlighted by the good water quality of the data set as whole. Although there was a trend to higher values of nitrogen forms during the dry season, significant statistical differences between the seasonal periods were reported only for the variables temperature and dissolved silica, which were higher in the dry and rainy season, respectively. The streams located in the preserved areas inside the ecological stations of APA Gama and Cabeça de Veado can still be considered good examples of reference lotic systems in the Cerrado Domain; notwithstanding, this study reported incipient signs of water quality degradation which cannot be overlooked in future limnological monitoring.     

Arsenic occurrence and accumulation in soil and water of eastern districts of Uttar Pradesh, India

Arsenic in the soil and water of eastern districts of Uttar Pradesh (Ballia and Ghazipur) was estimated. Survey results revealed that arsenic in soil samples ranged from 5.40 to 15.43 parts per million (ppm). In water samples, it ranged from 43.75 to 620.75 parts per billion (ppb) which far exceeded the permissible limit of 10 ppb as recommended by the World Health Organization. Maximum concentration of arsenic in water was found in Haldi village of Ballia (620.75 ppb). However, mean arsenic concentration in water followed the order: Karkatpur (257.21 ppb) < Haldi (310.15 ppb) < Sohaon (346.94 ppb) < Dharmarpur (401.75 ppb). In case of soil, maximum arsenic was detected in soil of Sohaon (15.43 ppm). Mean arsenic levels in soils followed the order: Karkatpur (9.24 ppm) < Haldi (9.82 ppm) < Dharmarpur (11.32 ppm) < Sohaon (14.08 ppm). Arsenic levels were higher in soils collected from 15–30 cm depth than 0–15 cm from the soil surface.     

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds?=?330 %), land-use intensity (odds?=?103 %), low soil quality (odds?=?49 %), slope (odds?=?29 %), and salinity of the groundwater (odds?=?26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.     

Planted floating bed performance in treatment of eutrophic river water

The objective of the study was to treat eutrophic river water using floating beds and to identify ideal plant species for design of floating beds. Four parallel pilot-scale units were established and vegetated with Canna indica (U1), Accords calamus (U2), Cyperus alternifolius (U3), and Vetiveria zizanioides (U4), respectively, to treat eutrophic river water. The floating bed was made of polyethylene foam, and plants were vegetated on it. Results suggest that the floating bed is a viable alternative for treating eutrophic river water, especially for inhibiting algae growth. When the influent chemical oxygen demand (COD) varied from 6.53 to 18.45 mg/L, total nitrogen (TN) from 6.82 to 12.25 mg/L, total phosphorus (TP) from 0.65 to 1.64 mg/L, and Chla from 6.22 to 66.46 g/m³, the removal of COD, TN, TP, and Chla was 15.3 %–38.4 %, 25.4 %–48.4 %, 16.1 %–42.1 %, and 29.9 %–88.1 %, respectively. Ranked by removal performance, U1 was best, followed by U2, U3, and U4. In the floating bed, more than 60 % TN and TP were removed by sedimentation; plant uptake was quantitatively of low importance with an average removal of 20.2 % of TN and 29.4 % of TP removed. The loss of TN (TP) was of the least importance. Compared with the other three, U1 exhibited better dissolved oxygen (DO) gradient distributions, higher DO levels, higher hydraulic efficiency, and a higher percentage of nutrient removal attributable to plant uptake; in addition, plant development and the volume of nutrient storage in the C. indica tissues outperformed the other three species. C. indica thus could be selected when designing floating beds for the Three Gorges Reservoir region of P. R. China.     

Water quality in Minho/Miño River (Portugal/Spain)

Minho River, also called Miño (in Spain), extends to about 300 km from Spain to Portugal. The source of the river lies in Spain and in the last 75 km, the river defines the border between Portugal and Spain. Under the scope of a cooperation project between North Portugal and Galicia region of Spain, titled: “Valorization of the natural resources of the Minho/Miño drainage basin”, seven water-sampling campaigns were carried out during the last 2 years in Minho River basin. Seven sampling sites were selected along the international stretch, and five were chosen in the main Portuguese and Spanish tributaries of Minho River. Water quality based on the physicochemical and microbial parameters was assessed. According to the Portuguese legislation for surface waters, the international section of Minho River presents a reasonably good water quality (BOD₅ <5 mg/L, TNK <2 mg/L, and total phosphorous <1 mg P/L). Valença and Louro were found to be the most polluted sampling sites and Louro the most polluted tributary (maximum values observed: TSS?=?26 mg/L, BOD₅?=?6.6 mg O₂/L, COD?=?20.8 mg O₂/L, total nitrogen?=?9.9 mg N/L; minimum value observed: OD?=?1.3 mg O₂/L). A one-dimensional stream water quality model QUAL2Kw was calibrated using data measured in field surveys along the international stretch of Minho River. QUAL2Kw was also used to predict the impact of flow conditions, discharges, and tributaries on the water quality of international stretch of Minho River, essential to establish proposals for management and planning of Minho River Basin.     

Carbon monoxide concentrations in outdoor wood-fired kitchens in Ouagadougou,Burkina Faso—implications for women’s and children’s health

A majority of households in developing countries rely on biomass fuel for cooking, typically burned in open fires or simple stoves. The incomplete combustion of these fuels causes adverse health effects such as respiratory diseases, especially among women and children. However, quantitative data on pollution levels and on associated diseases are limited. We examined cooking habits and self-reported health in 31 households with outdoor open wood fires in Ouagadougou, Burkina Faso, using structured interviews. In eight households, carbon monoxide (CO) was measured using passive sampling. In addition, meteorology and ambient CO concentrations were assessed. The average CO concentration during cooking was 4.3 ppm, with a maximum of 65.3 ppm and minimum of 0.3 ppm (1-min values). A clear daily pattern was observed, with relatively low concentrations during the day and high during the evening, occasionally exceeding the World Health Organization 1- and 8-h guidelines when the air stabilised. On average, CO concentrations were 43 % higher in kitchens located in closed yards than in those located in open yards, showing that fireplace location affected the levels. Eye irritation and coughing among women and children were reported by 30 % of the households. Based on previously reported relations between CO concentrations and fine particles (<2.5 μm), the exposure to biomass smoke appears to be high enough to pose a considerable health risk among women and children in households with outdoor open wood fires. The results suggest that burning should be limited between sunset and dawn and in areas with limited ventilation to reduce pollutions levels.