Petroleum refining and petrochemical complexes are significant sources of air pollution, emitting a variety of harmful pollutants with substantial health risks for nearby populations. While much of the information regarding this issue and the potential health impacts of this pollution has been documented, it remains fragmented across studies focusing on specific regions or health outcomes. These studies are often clustered into meta-analyses or reviews or exist as undeclared knowledge held by experts in the field, making it difficult to fully grasp the scope of the issue. To address this gap, our review consolidates the existing knowledge on the sources of air pollution from petroleum refining and petrochemical industries, the main pollutants involved, and their associated health outcomes. Additionally, we conducted an umbrella review of systematic reviews and meta-analysis and also included critical reviews. With this approach, we identified 12 reviews that comprehensively evaluate the health impacts in populations living near petroleum refining and/or petrochemical complexes. These reviews included studies spanning several decades (from 1980 to 2020) and encompassing regions across North America, Europe, Asia, South America, and Africa, reflecting diverse industrial practices and regulatory frameworks. From these studies, our umbrella review demonstrates that residents living near these facilities face elevated risks related to leukemia, lung and pancreatic cancer, nonmalignant respiratory conditions (such as asthma, cough, wheezing, bronchitis, and rhinitis), chronic kidney disease, and adverse reproductive outcomes. Furthermore, we discuss the key challenges in mitigating these health impacts and outline future directions, including the integration of cleaner technologies, which can significantly reduce harmful emissions; strengthening policy frameworks, emphasizing stringent emission limits, continuous monitoring, and regulatory enforcement; and advancing research on underexplored health outcomes. This review emphasizes the need for coordinated global efforts to align the industry’s evolution with sustainable development goals and climate action strategies to protect the health of vulnerable communities.

O ensino do controle de processos químicos carece de experimentos reprodutíveis. este trabalho objetivou implementar o controle de temperatura e umidade no ar em estufa. a instrumentação foi montada usando hardware livre, constituindo-se de um psicrômetro microcontrolado (atmel 328p), como unidade de aquisição de dados e controle. havia 4 sensores de temperaturas de bulbo seco e úmido (lm35, texas instruments.). a atuação na retroalimentação se deu pela manipulação da potência de aquecimento resistivo e da velocidade de uma ventoinha. o monitoramento se fez por bluetooth, entre o hardware livre mestre, e o software cliente no s.o. android. foram referências um termohigrômetro minipa mt-242 e um sensor dht11. as medidas de umidade diferiram de 0,4% até 9,1%, em uma média de 1000 medições. a identificação e a sintonia foram segundo cohen-coon e ziegler-nichols. o experimento agregou ao ensino de engenharia química as atividades práticas de projeto, sintonia e teste de controladores automáticos de temperatura e umidade do ar.

Milk adulteration represents a major challenge for food quality control because of its effects on consumer safety, nutritional value, and product authenticity. This study presents a comparative computational re-analysis of two previously published datasets addressing milk adulteration and fat-content classification using Electrical Impedance Spectroscopy (EIS)-derived and physicochemical variables. For the adulteration dataset (Dataset Q), supervised classifiers were reassessed using repeated stratified cross-validation, nested hyperparameter optimization, frequency-wise validation, statistical inference, and model interpretability analyses. Mean macro-F1 values under repeated cross-validation were 0.6478 for Support Vector Machine (SVM), 0.6456 for K-Nearest Neighbors (KNN), and 0.6310 for Random Forest, with substantial class-specific limitations. In particular, unadulterated and hydrogen-peroxide-adulterated milk showed persistent overlap and poor discrimination, consistent with the inferential analysis of the impedance-derived variables. LIME analysis of a representative Random Forest further indicated a strong predominance of Z1 in the model's local decision structure, accounting for 93.81% of the relative mean absolute LIME contribution. For the fat-content dataset (Dataset G), the original hold-out workflow yielded complete classification of the 34 validation observations by KNN and high cluster-to-class agreement for Gaussian Mixture and K-means models. However, these results require cautious interpretation because the input feature space included whole-milk volume, a formulation-related variable directly associated with the definition of the fat-content classes. Overall, the re-analysis demonstrates that aggregate performance metrics alone may obscure important limitations in small milk-quality datasets and highlights the importance of robust validation, class-specific assessment, and careful evaluation of predictor independence when combining EIS-derived information with machine learning.