Environment Pollution and Climate Change
Open Access

Air pollution; Environmental pollution sources

Introduction

In cross-sectional analysis, we found no association between each air pollutant and A1-42 or the ratio measures after controlling for socio-demographic and behavioral covariates. We found stronger positive associations between each air pollutant and all three outcomes through repeat measures analysis. We saw an increase of 4.43 percent (95 percent CI: 3.26 percent, 5.60 percent), 9.73 percent (6.20%, 13.38 percent), and 1.57% (95 percent CI: a 2.20 percent (0.94%) higher A1- 42/A1-40 ratio and a 2 g/m3 higher 20-year average PM2.5 Similar relationships existed with other air pollutants. By evaluating longer air pollution exposure averaging periods to better mimic disease progression and providing a modifiable target for ADRD prevention, our study adds to the growing body of evidence on air pollution and ADRD biomarkers.

The world’s population is getting older quickly. According to Nicholas et al., the global proportion of people 65 and older is expected to rise from 8% in 2010 to 16% by 2050. 2022). With the maturing populace, instances of dementia are likewise expected to rise, bringing about an expected 152.8 million cases, a significantly increasing, by 2050. There are no known treatments for Alzheimer’s disease and other dementias (ADRD), but several risk factors can be changed. Bhatti et al., in addition to lifestyle factors like diet and exercise, ADRD has also been linked to environmental dangers like air pollution.

Dementia is caused by air pollution, but the exact mechanism has not yet been determined. According to a number of animal studies air pollution’s effects on the central nervous system (CNS) have been linked to neuroinflammation and oxidative stress, two factors that play a crucial role in ADRD development. Air pollution may also increase amyloid beta (A) deposition in the brain, a hallmark of Alzheimer’s disease (AD), by altering the activity of key enzymes. According to Patten et al.’s research on animals, animals exposed to traffic-related air pollution (TRAP) developed more A plaques over time than animals exposed to filtered air.

Although it has been debated whether treatments that reduce A plaques can improve cognitive function, understanding how air pollution affects brain health remains a significant undertaking. According to Peters et al., the peptide A-1-40, which has a 40-amino acid chain, has been linked to diabetes and cardiovascular disease. Other disease processes can also be better understood by studying A1- 40 and air pollution. On the other hand, A1-42 is a crucial component of brain a plaques [1-5].

As described in Chapter 5, we used air pollution maps with annual mean concentrations of NO2, PM2.5, PM10, and daily maximum hourly (Dmax) NO2 at 10-km/2-km resolution. We used both the long-term metric (the April–September average of the daily maximum 8-hour mean above 35 p.p.b.) and the short-term metric (O3) for O3. equivalent to 70 g/m3)] as well as the short-term metric, which is the annual average of the daily maximum 8-hour mean, without a threshold or cut-off point. We combined the concentrations of modelled air pollution up to the level in 2011 The scenarios for the future have already been discussed.

Air pollution poses a significant threat to human health and has an impact on nearly every living thing and organ. Fortunately, most of the risk can be avoided. It is possible to have a rapid and significant impact on health by reducing pollution at its source. Symptoms of respiratory irritation, such as shortness of breath, cough, phlegm, and a sore throat, disappear within a few weeks; Premature births, cardiovascular disease and death, hospitalizations, school absenteeism, and all-cause mortality all decrease significantly. Cost-effective are the interventions. Strong cobenefits result from reducing climate change and air pollution. Health improvements continue to be associated with pollution reductions even below international standards, despite the fact that high-pollution areas have the greatest potential for health benefits. Both the urgency of improving global air quality and the significance of intensifying efforts to reduce pollution at the local level are emphasized by the rapid response to these interventions and the short amount of time required for their benefits.

Discussion

Plasma is expected to contain A, in contrast to the presence of A plaques in the brain. According to Lopez et al., plasma A biomarkers change over time and can be affected by a variety of factors and have distinct outcomes for individuals with intact cognitive function and those with dementia. Plasma A levels rise with age in cognitively intact individuals, whereas plasma A biomarkers decrease over time in those who go from intact to MCI to AD. The blood-brain barrier allows A from the brain to travel to the blood and vice versa. In healthy people, the brain’s soluble A could be cleared into the blood, resulting in higher plasma levels; however, in sick people, the brain’s soluble A becomes insoluble A plaques, which the body is unable to clear, resulting in lower plasma levels.

In light of the fact that higher levels of pollution in the air have been shown to worsen outcomes, we would anticipate that higher levels of air pollution would be linked to lower plasma A levels in those who go on to develop dementia and higher plasma A levels in those who do not develop the disease. Plasma A levels that are higher may indicate that air pollution is accelerating plasma A levels, which may then lead to worse cognitive health.

Only a few studies have looked into the connection between air pollution and A in humans. Some of these studies have used PET scans or MRI to identify A, CSF (cerebrospinal fluid) from other sources. In general, the study finds a positive correlation between A and air pollution; The association is more consistent in studies that use imaging data. Even though these methods are effective at identifying A, they lack the ease, cost, and participant burden of blood biomarkers of AD. Plasma-based A biomarkers have not been the subject of any research, to our knowledge.

In the hope of developing a diagnostic test that will make it simpler to identify ADRD, researchers continue to look for highly sensitive methods of measuring A in the blood. These methods will enable a deeper comprehension of the mechanisms underlying the observed links between dementia and air pollution once they have been identified. Plasma-based A biomarkers and air pollution were the subjects of this cross-sectional and repeated-measures investigation in a group of dementia-free older adults.

In sensitivity analyses, we looked at different ways to make sure the outcome residuals were normal, specifically trimming (or excluding) the extreme outliers without changing the distribution. When outliers were specifically excluded in the repeat measures analysis, this did not significantly alter the results, but it did result in a slight attenuation of parameter estimates.

By only looking at those participants who provided samples to both the baseline and the 2007–2008 visit (i.e., those who were not censored or lost to follow-up), we also looked into the possibility of selection bias. In comparison to baseline, we observed stronger associations between all outcomes measured at follow-up and PM2.5 and NO2. The PM10 results did not change much. This suggests that participants who remained in the study until the end of the follow-up period were the driving force behind the results of our repeated measures analysis.

When compared to the cross-sectional results, our repeated measures analysis may have produced stronger associations for a number of different reasons. First, the larger sample size in the repeated measures analysis gives us more power to detect an effect. Because we are more certain about residential histories during follow-up and the several years prior to GEMS enrollment (e.g., approximately 1998 - 2008 for the 10 year averaging period) in comparison to residential histories prior to GEMS enrollment (e.g., approximately 1990 - 2000 for the 10 year averaging period), those in our repeated measures analysis [6-10].

Conclusion

Furthermore, historical models that are based on less monitoring data and are therefore more prone to measurement error are used for exposure assessment further back in time. In the repeat measures analysis, estimates with larger magnitudes may be produced by both increased power and decreased exposure measurement error. Because plasma biomarker data fluctuates significantly and some disease-free participants experience decreases in A during follow-up, we chose to conduct a repeat measures analysis rather than one that evaluates change in A over time. This is to be expected given plasma A’s dynamic nature (described above). In addition, the repeat measures approach enables us to have a sample that is more generalizable and has more power due to the loss of follow-up in our sample.

1. Jurate V, Mika S, Petri L (2002) Electrokinetic soil remediation--critical overview. Sci Total Environ 289: 97-121.

Indexed at, Google Scholar, Crossref

2. Zhiping S, Hui Z, Yunhong Z (2010) Polyimides: Promising energy-storage materials. Angew Chem Int Ed 49: 8444 - 8448.

Indexed at, Google Scholar, Crossref

3. Cavallaro G, Lazzara G, Milioto S (2010) Dispersions of Nanoclays of Different Shapes into Aqueous and Solid Biopolymeric Matrices. Extended Physicochemical Study. J Surf Colloids 27: 1158-1167.

Indexed at, Google Scholar, Crossref

4. Lee J, Cameron I, Hassall M (2019) Improving process safety: what roles for digitalization and industry 4.0? Process Saf Environ Prot 132: 325 - 339.

Indexed at, Google Scholar, Crossref

5. Baraud F, Tellier S, Astruc M (1997) Ion velocity in soil solution during electrokinetic remediation. J. Hazard Mater 56: 315-332.

Google Scholar, Crossref

6. Hong Ji, Weiqiu Huang, Zhixiang Xing, Jiaqi Zuo, Zhuang Wang,  et al. (2019) Experimental study on removing heavy metals from the municipal solid waste incineration fly ash with the modified electrokinetic remediation device. Sci Rep 9: 8271.

Indexed at, Google Scholar, Crossref

7. Le Borgne S, Paniagua D, Vazquez-Duhalt R (2008) Biodegradation of organic pollutants by halophilic Bacteria and Archaea. J Mol Microbiol Biotechnol 15: 74-92.

Indexed at, Google Scholar, Crossref

8. Agamuthu P, Abioye OP, Aziz AA (2010) Phytoremediation of soil contaminated with used lubricating oil using Jatropha curcas. J Hazard Mater 179: 891-894.

Indexed at, Google Scholar, Crossref

9. Bergerson JA, Keith D (2010) The truth about dirty oil: is CCS the answer? Environ Sci Technol 44: 6010 -6015.

Indexed at, Google Scholar, Crossref

10. Carlson HK, Stoeva MK, Justice NB, Sczesnak A, Mullan MR, et al. (2015) Monofluorophosphate is a selective inhibitor of respiratory sulfate‐reducing microorganisms. Environ Sci Technol 49: 3727-3736.

Indexed at, Google Scholar, Crossref