Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan.

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O3) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NOX) and meteorology. Considering the large contribution of O3 in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NOX and O3 from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O3 formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOCcons) which was followed by the source identification of their initial concentrations. VOCcons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O3 levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O3 (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O3-pollution at the study location-ns as well as their accumulation in distant regions.

Climate Change, Migration, and Health Disparities at and Beyond the US-Mexico Border.

This JAMA Insights in the Climate Change and Health series discusses the increase in extreme weather events caused by climate change and how these events bring about increased migration due to effects on water availability, food access, and rates of endemic diseases.

Assessment of indoor radon distribution and seasonal variation within the Kpando Municipality of Volta Region, Ghana.

This study uses CR-39 radon detectors to examine radon distributions, seasonal indoor radon variations, correction factors, and the influence of building materials and characteristics on indoor radon concentration in 120 dwellings. The study also determines the spatial distribution of radon levels using the ArcGIS geostatistical method. Radon detectors were exposed in bedrooms from April to July (RS), August to November (DS); December to March (HS), and January-December (YS) from 2021 to 2022. The result for the radon levels during the weather seasons were; 32.3 to 190.1 Bqm-3 (80.9 ± 3.2 Bq/m3) for (RS), 30.8 to 151.4 Bqm-3 (68.5 ± 2.7 Bqm-3) for HS and 24.8 to 112.9 Bqm-3(61.7 ± 2.1 Bqm-3) for DS, and 25.2 to 145.2 Bq/m3 (69.4 ± 2.7 Bqm-3). The arithmetic mean for April to July season was greater than August to November. The correction factors associated with this study ranged from 0.9 to 1.2. The annual effective dose (AE) associated with radon data was varied from 0.6 to 4.04 mSv/y (1.8 ± 0.1 mSv/y). The April to July period which was characterized by rains recorded the highest correlation coefficient and indoor radon concentration. Distribution and radon mapping revealed radon that the exposure to the occupant is non-uniformly spread across the studied dwellings. 15.4% of the studied data exceeded WHO reference values of 100 Bq/m3. The seasonal variation, dwelling age, and building materials were observed to have a substantial impact on the levels of radon concentration within the buildings.

Satellite microwave measurements complementary to fire weather improve the assessment of fires among different biomes in Southeast Asia.

Microwaves have the advantage of penetrating vegetation and exhibit sensitivity to properties such as vegetation water content (VWC); yet, their potential utility in the fire domain is infrequently investigated. This study elucidates the different impacts of the microwave VWC index EDVI on fire radiative energy (FRE) across various biome types and the significant predictive power for high-severity fires (defined based on FRE) in mainland Southeast Asia. While EDVI exhibits lower predictive power for high severe fires compared to the commonly used fire weather indices (e.g., FWI), an enhancement is observed when these predictors are used in combination. Either by employing EDVI or fire weather indices, the predictability of fires is found to be highest over forests and lowest over croplands. Factors such as increasing human influence and fuel limitation in croplands are likely reducing the roles of VWC and weather on fires, contributing to the lower prediction skill of EDVI and fire weather. These results indicate the usefulness of microwave VWC index in fire studies. Although fire weather presents more considerable impacts on fires, the microwave VWC index seem to still provide some complementary information in fire danger assessment.

Contextual Factors to Improve Implementation of Malaria Chemoprevention in Children: A Systematic Review.

Malaria remains a leading cause of childhood morbidity and mortality in sub-Saharan Africa, particularly among children under 5 years of age. To help address this challenge, the WHO recommends chemoprevention for certain populations. For children and infants, the WHO recommends seasonal malaria chemoprevention (SMC), perennial malaria chemoprevention (PMC; formerly intermittent preventive treatment in infants [IPTi]), and, more recently, intermittent preventive treatment in school children (IPTsc). This review describes the contextual factors, including feasibility, acceptability, health equity, financial considerations, and values and preferences, that impact implementation of these strategies. A systematic search was conducted on July 5, 2022, and repeated April 13, 2023, to identify relevant literature. Two reviewers independently screened titles for eligibility, extracted data from eligible articles, and identified and summarized themes. Of 6,295 unique titles identified, 65 were included. The most frequently evaluated strategy was SMC (n = 40), followed by IPTi (n = 18) and then IPTsc (n = 6). Overall, these strategies were highly acceptable, although with IPTsc, there were community concerns with providing drugs to girls of reproductive age and the use of nonmedical staff for drug distribution. For SMC, door-to-door delivery resulted in higher coverage, improved caregiver acceptance, and reduced cost. Lower adherence was noted when caregivers were charged with giving doses 2 and 3 unsupervised. For SMC and IPTi, travel distances and inclement weather limited accessibility. Sensitization and caregiver education efforts, retention of high-quality drug distributors, and improved transportation were key to improving coverage. Additional research is needed to understand the role of community values and preferences in chemoprevention implementation.

Assessment of body fluid identification and DNA profiling after exposure to tropical weather conditions.

Despite current advances in body fluid identification, there are few studies evaluating the effect of environmental conditions. The present work assessed the detection of body fluids, blood, semen, and saliva, through lateral flow immunochromatographic (LFI) tests, exposed to tropical weather conditions over time, also evaluating the possibility of obtaining STR (short tandem repeat) profiles and identifying mitochondrial DNA (mtDNA) polymorphisms. Blood, semen, saliva samples, and mixtures of these fluids were deposited on polyester clothes and exposed to open-air tropical weather conditions for 1 month. The test versions from LFI (SERATEC®, Germany) Lab and crime scene (CS) used for the detection - one per each body fluid type - demonstrated that it is possible to identify body fluids and their mixtures up to 14 days after deposition. At 30 days, blood and semen were detected but not saliva. Full STR profiles were obtained from 14-day-old blood samples, and partial profiles were obtained from the remaining samples. It was possible to sequence mtDNA in the samples previously analyzed for STR profiling, and haplogroups could be assigned. In conclusion, this study demonstrated for the first time the possibility of body fluid identification and DNA profiling after exposure to tropical weather conditions for 1 month and also demonstrated the value of mtDNA analysis for compromised biological evidence.

Inflow and infiltration assessment of a prototype sanitary sewer network in a coastal city in China.

A 16-month monitoring program was conducted on a prototype sanitary system in a coastal city in China. The groundwater infiltration (GWI) on dry weather days and the rain-derived inflow and infiltration (RDII) on wet weather days were quantified and analyzed. The proportion of monthly averaged GWI to total flow can be as high as 70% during the observation period mainly due to the high groundwater level. The results also show that the ratio of RDII volume to total rainfall volume (defined as R-value) reaches a limited value of approximately 10% for the studied system when the total rainfall depth increases. A reference indicator Rlim for the limited R-value was proposed for assessing the conditions of sewer systems in terms of RDII. The Rlim value depends on local sewer conditions and in general, a lower Rlim value represents a better performance on RDII and vice versa. This study enriches the case studies on the performance of a specific sanitary sewer system on inflow and infiltration in a typical coastal city with exceptionally high groundwater levels, excess rainfall events in the monitoring season and possible typhoon events, which addresses the unique locational and hydrological properties of a representative coastal city.

Exploring interactions between socioeconomic context and natural hazards on human population displacement.

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.

Momentary within-subject associations of affective states and physical behavior are moderated by weather conditions in real life: an ambulatory assessment study.

BACKGROUND: Physical behavior (PB) is a key lifestyle factor in regulating and preventing diseases across the lifespan. Researchers identified affective, cognitive, and contextual factors like weather conditions, as significant contributors in determining if individuals are physically active. However, there is scarce empirical evidence about potential associations between PB and affective states influenced by weather conditions in daily life. Therefore, we explored if weather conditions moderated the within-subject association between momentary affective states and subsequent PB. METHODS: Utilizing ambulatory assessment, 79 participants completed electronic diaries about their affective states (i.e., valence, energetic arousal, and calmness) up to six times a day over five days, and their PB (i.e., physical activity and sedentariness) was simultaneously recorded via accelerometers. Weather conditions (i.e., temperature and precipitation) recorded near participants' locations served as moderators in the multilevel analyses. RESULTS: We confirmed earlier findings associating affective states with PB. Increased valence and energetic arousal were positively associated with physical activity (ß = 0.007; p < .001), whereas calmness predicted lower levels of physical activity (ß = -0.006; p < .001). Higher levels of calmness showed a positive association with sedentary behavior (ß = 0.054; p = .003). In addition, we revealed a significant positive association between temperature, as a momentary weather condition, and physical activity (ß = 0.025; p = .015). Furthermore, we showed that the association of affective states and physical activity was moderated by temperature. Higher temperatures enhanced the positive effects of valence on physical activity (ß = .001, p = .023) and attenuated the negative effects of calmness on physical activity (ß = .001, p = .021). Moreover, higher temperatures enhanced the positive effects of valence on reduced sedentary behavior (ß = -0.011, p = .043). CONCLUSIONS: Temperature alterations appeared to have an impact on subsequent physical activity. Furthermore, temperature alterations moderated the influence of affective states on conducted physical activity. This might offer the opportunity for just-in-time adaptive interventions to intervene in individually appropriate environmental conditions for promoting physical activity.

Exergo-enviro-economic and yearly productivity analyses of conical passive solar still for sustainable solar distillation.

This paper focuses on exergo-enviro-economic and yearly productivity analyses for conical passive solar still having the potential to fulfil the sustainable development goal of the United Nations. A new approach for thermal modelling of conical passive solar still has been carried out with experimental validation in the present work, wherein different weather conditions have been considered for the analysis of the proposed system. The carried out work has been done for each month of the year. In further methodology, the computational code in MATLAB has been used for the computation of hourly freshwater production, exergy, and energy followed by the estimation of their annual values. Thereafter, exergo-enviro-economic parameters, yearly productivity, payback period, and freshwater cost have been estimated, and the obtained results have been compared with the earlier published research. Concludingly, the exergo-economic parameter, enviro-economic parameter, and yearly productivity for the proposed system have been found higher by 44.25%, 25.68%, and 44.07%, respectively, than the conventional solar still. The comparative freshwater cost is 13.56% less than the conventional solar still for 0.025 m water depth. Additionally, the payback period for the proposed system will remain at 2.75 years, which is 13.82% less in comparison to the conventional solar still considering a 2% interest rate.

The validity of Okun's Law in the case of Central Asia: The role of weather and energy factors.

This study investigates a modified version of the Okun's Law that incorporates energy consumption and temperature for five Central Asian countries, while it employs the ARDL methodology, spanning the period 1995-2018. While the original Okun's Law is not supported, the analysis does find support for the modified Law, suggesting the importance of regional specific factors. The results document the presence of a negative association between unemployment and real GNP both in the short and in the long run. The impact of energy consumption on unemployment is positive in the long run, while the temperature impacts unemployment both in the short and in the long run.

Assessment of the effect of short-term weathering on the molecular-level chemical composition of crude oils in contact with aquatic environments.

Multiple studies have focused on the effect of long-term weathering processes on oils after spill events, without considering the chemical compositional changes occurring shortly after the release of oil into the environment. Therefore, the present study provides a broad chemical characterization for understanding of the changes occurring in the chemical compositions of intermediate (°API = 27.0) and heavy (°API = 20.9) oils from the Sergipe-Alagoas basin submitted to two simulated situations, one under marine conditions and the other in a riverine environment. Samples of the oils were collected during the first 72 h of contact with the simulated environments, followed by evaluation of their chemical compositions. SARA fractionation was used to isolate the resins, which were characterized at the molecular level by UHRMS. The evaporation process was highlighted, with the GC-FID chromatographic profiles showing the disappearance of compounds from n-C10 until n-C16, as well as changes in the weathering indexes and pristane + n-C17/phytane + n-C18 ratios for the crude oils submitted to the riverine conditions. Analysis of the resins fraction showed that basic polar compounds underwent little or no alterations during the early stages of weathering. The marine environment was shown to be much less oxidative than the riverine environment. For both environments, a feature highlighted was an increase of acidic oxygenated compounds with the increase of weathering, especially for the crude oil with °API = 27.0.

Tourniquet self-application assessment in cold weather conditions.

BACKGROUND: Our study aimed to assess the ability of nonmedical civilians to self-apply extremity tourniquets in cold weather conditions while wearing insulating technical clothing after receiving basic training. METHODS: A field study was conducted among 37 voluntary participants of an expedition party to the Spanish Antarctic base. The researchers assessed the participant's ability to self-apply five commercial extremity tourniquets (CAT, OMNA, RMT, SWAT-T, and RATS) over cold-weather clothing and their achieved effectiveness for vascular occlusion. Upper extremity self-application was performed with a single-handed technique (OHT), and lower extremity applying a two-handed technique (THT). Perceptions of self-application ease mean values ± standard deviation (SD) were compared by applying a 5% statistical significance threshold. Frequency count determined tourniquet preference. RESULTS: All the tested ETs, except the SWAT-T, were properly self-applied with an OHT, resulting in effective vascular occlusion in the upper extremity. The five devices tested were self-applied correctly in the lower extremities using THT. The ratcheting marine-designed OMNA ranked the highest for application easiness on both the upper and lower extremities, and the windlass CAT model was the preferred device by most participants. CONCLUSIONS: Civilian extremity tourniquet self-application on both upper and lower extremities can be accomplished in cold weather conditions despite using cold-weather gloves and technical clothing after receiving brief training. The ratcheting marine-designed OMNA ranked the highest for application ease, and the windlass CAT model was the preferred device.

Improvement in the Post-Processing of Wave Buoy Data Driven by the Needs of a National Coast and Sea Monitoring Agency.

Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys' data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions.

Impact of climate change on SARS-CoV-2 epidemic in China.

The outbreak and prevalence of SARS-CoV-2 have severely affected social security. Physical isolation is an effective control that affects the short-term human-to-human transmission of the epidemic, although weather presents a long-term effect. Understanding the effect of weather on the outbreak allow it to be contained at the earliest possible. China is selected as the study area, and six weather factors that receive the most attention from January 20, 2020 to April 30, 2020 are selected to investigate the correlation between weather and SARS-CoV-2 to provide a theoretical basis for long-term epidemic prevention and control. The results show that (1) the average growth rate (GR) of SARS-CoV-2 in each province is logarithmically distributed with a mean value of 5.15%. The GR of the southeastern region is higher than that of the northwestern region, which is consistent with the Hu Line. (2) The specific humidity, 2-m temperature (T), ultraviolet (UV) radiation, and wind speed (WS) adversely affect the GR. By contrast, the total precipitation (TP) and surface pressure (SP) promote the GR. (3) For every 1 unit increase in UV radiation, the GR decreases by 0.30% in 11 days, and the UV radiation in China is higher than that worldwide (0.92% higher per day). Higher population aggregation and urbanization directly affect the epidemic, and weather is an indirect factor.

Extreme weather raises the prices of regional emission allowances in China.

Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China's regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations.

Relating biogas technology and environmental impact assessment: a roadmap towards clean energy for environmental sustainability.

Atmosphere contamination and fast weather change have compelled scientists and entrepreneurs to seek eco-friendly solutions for saving the earth. Increasing energy consumption depletes limited natural resources and harms the climate and ecology. In this regard, biogas technology contributes in two ways: satisfying energy needs and saving plants. Pakistan is a farming nation with enormous biogas-based energy generating potential. The primary objectives of this study are to identify the most significant barriers to farmers' investment in biogas technology. Non-probability sampling (purposive sampling) was utilized to establish the sample size. Ninety-seven investors and farmers were systematically sampled in biogas technology engaged in this survey. The planned questionnaire was practiced to obtain key facts via online interviews. The partial least square structural equation modeling (PLS-SEM) was used to evaluate designated hypotheses. The current research concludes that entire autonomous variables are substantial and interrelated with investment in biogas machinery, dropping energy disasters, and accomplishing environmental, financial, and maintenance government support objectives. The results also revealed that electronic and social media play a moderating influence. This conceptual model is considerably and positively affected by the chosen factors and their moderation. This study concludes that the primary tools for attracting farmers and investors in biogas technology are proper biogas technology awareness with relevant experts, financial and maintenance government responsibility, user efficiency and environmental concern of biogas plants, and the role of electronic and social media. The results also advised that the government should implement an incentive and maintenance policy to entice new farmers and investors in Pakistan to build biogas technology. Finally, the study's limitations and recommendations for additional studies are highlighted.

Impact assessment of natural variations in different weather factors on the incidence of whitefly, Bemisia tabaci Genn. and yellow vein mosaic disease in Abelmoschus esculentus (L.) Moench.

Bemisia tabaci Gennadius, also renowned as the silver leaf whitefly, is among the most damaging polyphagous insect pests in many commercially important crops and commodities. A set of field experiments were conducted for three consecutive years i.e., 2018-2020, to investigate the role of variations in rainfall, temperature, and relative humidity on the abundance of B. tabaci in okra (Abelmoschus esculentus L. Moench). In the first experiment, the variety Arka Anamika was cultivated twice a year to analyse the incidence of B. tabaci concerning the prevailing weather factors and the overall pooled incidence recorded during the dry and wet season was 1.34 ± 0.51 to 20.03 ± 1.42 and 2.26 ± 1.08 to 18.3 ± 1.96, respectively. Similarly, it was observed that the highest number of B. tabaci catch (19.51 ± 1.64 whiteflies/3 leaves) was recorded in morning hours between 08:31 to 09:30 a.m. The Yellow Vein Mosaic Disease (YVMD) is a devastating disease of okra caused by begomovirus, for which B. tabaci acts as a vector. In another experiment, screening was conducted to check the relative susceptibility of three different varieties viz., ArkaAnamika, PusaSawani, and ParbhaniKranti against B. tabaci (incidence) and YVMD ((Percent Disease Incidence (PDI), Disease Severity Index (DSI), and Area Under the Disease Progress Curve (AUDPC)). The recorded data was normalized by standard transformation and subjected to ANOVA for population dynamics and PDI. Pearson's rank correlation matrix and Principal Component Analysis (PCA) have been used to relate the influences of various weather conditions on distribution and abundance. SPSS and R software were used to create the regression model for predicting the population of B. tabaci. Late sown PusaSawani evolved as a highly susceptible variety in terms of B. tabaci (24.83 ± 6.79 adults/3leaves; mean ± SE; N = 10) as well as YVMD i.e., PDI (38.00 ± 4.95 infected plants/50plants), DSI (71.6-96.4% at 30 DAS) and AUDPC (mean ß-value = 0.76; R2 = 0.96) while early sown Parbhani Kranti least susceptible to both. However, the variety ArkaAnamika was observed as moderately susceptible to B. tabaci and its resultant disease. Moreover, environmental factors were predominantly responsible for regulating the population of insect pests in the field and hence its productivity like rainfall and relative humidity were negative while the temperature was positively correlated with B. tabaci (incidence) and YVMD (AUDPC). The findings are helpful for the farmers to choose need-based IPM strategies than timing-based, which would fit perfectly with the present agro-ecosystems in all ways.

Economic and racial disparities of the weather impact on air quality in Brazil.

Several studies have quantified the air pollution exposure disparities across racial and income groups. However, there is still a lack of investigations assessing disparities related to the impacts of weather on air pollution, which could indicate target air pollution reduction strategies under different climate scenarios. Our study aims to address this gap by estimating the economic and racial disparities of the weather impact on air quality in Brazil between 2003 and 2018. First, we used a generalized additive approach to estimate the weather-related changes in PM2.5. This framework derived "weather penalty", which a positive penalty suggests that an increase in PM2.5 was associated with long-term weather changes in the study period. Then, we estimated the population-weighted weather penalty for racial and income groups. Average penalty for the White population (the most-exposed group) was 31% higher than that of the Pardo population (the least-exposed group, mainly people of light brown skin color) in Brazil. In the stratification analysis by region, the Midwest and South were the regions where the black population was the most-exposed group. For the income group, our results indicate that the high-income population group was the most-exposed group in all analyses, including the national and the regional analyses. These findings are somewhat surprising, as previous studies have shown that minority and low-income populations tend to be more exposed to air pollution, than white and higher-income populations. However, our study suggests that disparities in exposure to air pollution may be more complex and nuanced than previously thought. Further research is needed to better understand the underlying drivers of these environmental disparities, and to develop targeted interventions to reduce exposures.

How heat stress conditions affect milk yield, composition, and price in Italian Holstein herds.

An edited data set of 700 bulk and 46,338 test-day records collected between 2019 and 2021 in 42 Holstein-dominated farms in the Veneto Region (North of Italy) was available for the present study. Information on protein, fat and lactose content, somatic cell count, and somatic cell score was available in bulk milk as well as individual test-day records, whereas urea concentration (mg/dL), differential somatic cell count (%), and milk yield (kg/d) were available for test-day records only. Milk features were merged with meteorological data retrieved from 8 weather stations located maximum 10 km from the farms. The daily and weekly temperature-humidity index (THI; wTHI) and maximum daily (MTHI) and weekly temperature-humidity index were associated with each record to evaluate the effect of heat stress conditions on milk-related traits through linear mixed models. Least squares means were estimated to evaluate the effect of THI and, separately, of MTHI on milk characteristics correcting for conventional systematic factors. Overall, heat stress conditions lowered the quality of both bulk milk and test-day records, with fat and protein content being greatly reduced, and somatic cell score and differential somatic cell count augmented. Milk yield was not affected by either THI or MTHI in this data set, but the effect of elevated THI and MTHI was in general stronger on test-day records than on bulk milk. Farm-level economic losses of reduced milk quality rather than reduced yield as consequence of elevated THI or MTHI was estimated to be between $23.57 and $43.98 per farmer per day, which is of comparable magnitude to losses resulting from reduced production. Furthermore, MTHI was found to be a more accurate indicator of heat stress experienced by a cow, explaining more variability of traits compared with THI. The negative effect of heat stress conditions on quality traits commences at lower THI/MTHI values compared with milk yield. Thus, a progressive farmers' income loss due to climatic changes is already a reality and it is mainly due to deterioration of milk quality rather than quantity in the studied area.