Indoor and Outdoor Nanoparticle Concentrations in an Urban Background Area in Northern Sweden: The NanoOffice Study

In recent years, nanoparticles (NPs) have received much attention due to their very small size, high penetration capacity, and high toxicity. In urban environments, combustion-formed nanoparticles (CFNPs) dominate in particle number concentrations (PNCs), and exposure to those particles constitutes a risk to human health. Even though fine particles (<2.5 µm) are regularly monitored, information on NP concentrations, both indoors and outdoors, is still limited. In the NanoOffice study, concentrations of nanoparticles (10–300 nm) were measured both indoors and outdoors with a 5-min time resolution at twelve office buildings in Umeå. Measurements were taken during a one-week period in the heating season and a one-week period in the non-heating season. The measuring equipment SMPS 3938 was used for indoor measurements, and DISCmini was used for outdoor measurements. The NP concentrations were highest in offices close to a bus terminal and lowest in offices near a park. In addition, a temporal effect appeared, usually with higher concentrations of nanoparticles found during daytime in the urban background area, whereas considerably lower nanoparticle concentrations were often present during nighttime. Infiltration of nanoparticles from the outdoor air into the indoor air was also common. However, the indoor/outdoor ratios (I/O ratios) of NPs showed large variations between buildings, seasons, and time periods, with I/O ratios in the range of 0.06 to 0.59. The reasons for high indoor infiltration rates could be NP emissions from adjacent outdoor sources. We could also see particle growth since the indoor NPs were, on average, almost twice as large as the NPs measured outdoors. Despite relatively low concentrations of NPs in the urban background air during nighttime, they could rise to very high daytime concentrations due to local sources, and those particles also infiltrated the indoor air.

A Lane Extraction Algorithm Based on Fuzzy Set

Lanes are difficult to be extracted completely. A lane extraction algorithm is proposed according to vehicle driving rules. Vehicles are moving constantly, so the foreground area and background area cannot be defined effectively in the image. Therefore, based on the theory of fuzzy set, multidimensional degree is used to judge the membership degree of target and foreground in order to extract the moving area accurately. Then, the logistic regression model is established to determine the moving vehicles. Finally, based on the vehicle track, the lane extraction is realized by regional growth. The results show that the proposed algorithm can extract the road effectively.

Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods.

Evaluation of the Environmental Risk of Contaminated Materials: Advice on the Most Appropriate Environmental Remediation Techniques

This work addresses the contamination of the sediments of an alluvial plain and riverbed of a tributary of the San Francisco River, in the Brazilian state of Minas Gerais, by potentially toxic elements from an industrial unit of metallic alloys production. This area was subdivided into four areas (A1, A2, A3, and A0 (background area)) where sediment samples have been collected followed by geochemical characterization and spatial distribution of the contaminants. This characterization was based on the (1) analysis of dissolved elements in the interstitial waters, (2) identification of exchangeable and carbonates bounded fractions, and (3) leaching tests using deionized water adjusted to the local pH. This analysis revealed high levels mainly in Cd, Pb, and Zn, in the interstitial waters and in the more soluble phases of sediments. The comparison between the levels of these elements in the leached extracts and the more soluble fractions corroborates the high capacity of these elements to be leached from the alluvium following precipitation episodes. The geochemical characterization and spatial distribution of the contaminants will allow, in the near future, a choice of the most appropriate environmental remediation technique(s) for the environmental requalification of this area.

NRA-Net—Neg-Region Attention Network for Salient Object Detection with Gaze Tracking

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.

Source Apportionment and Assessment of Air Quality Index of PM2.5–10 and PM2.5 in at Two Different Sites in Urban Background Area in Senegal

Identifying the particulate matter (PM) sources is an essential step to assess PM effects on human health and understand PM’s behavior in a specific environment. Information about the composition of the organic or/and inorganic fraction of PM is usually used for source apportionment studies. In this study that took place in Dakar, Senegal, the identification of the sources of two PM fractions was performed by utilizing data on the elemental composition and elemental carbon content. Four PM sources were identified using positive matrix factorization (PMF): Industrial emissions, mineral dust, traffic emissions, and sea salt/secondary sulfates. To assess the effect of PM on human health the air quality index (AQI) was estimated. The highest values of AQI are approximately 497 and 488, in Yoff and Hlm, respectively. The spatial location of the sources was investigated using potential source contribution function (PSCF). PSCF plots revealed the high effect of transported dust from the desert regions to PM concentration in the sampling site. To the best of our knowledge, this is the first source apportionment study on PM fractions published for Dakar, Senegal.